Modular Deep Reinforcement Learning framework in PyTorch.
Documentation:
https://slm-lab.gitbook.io/slm-lab/
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| BeamRider | Breakout | KungFuMaster | MsPacman |
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| Pong | Qbert | Seaquest | Sp.Invaders |
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| Ant | HalfCheetah | Hopper | Humanoid |
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| Inv.DoublePendulum | InvertedPendulum | Reacher | Walker |
Describe the bug I'm enjoying the book a lot. The best book on the subject and I've read Sutton & Barto, but I'm an empiricist and not an academic. Anyway, I can run all the examples in the book in 'dev' and 'train' modes but not in 'search' mode. They all end with error. I don't see anybody complaining about this so it must be a rooky mistake on my part. I hope you can help so I can continue enjoying the book to its fullest.
To Reproduce
git rev-parse HEAD to get it): What?spec file used: benchmark/reinforce/reinforce_cartpole.jsonAdditional context I'm showing the error logs for Code 2.15 in page 50, but I get similar error logs for all the other codes ran in 'search' mode. There are 32 files in the 'data' folder, no plots. All the folders in the 'data' folder are empty except for 'log' which has a file with this
[2020-01-30 11:03:56,907 PID:3351 INFO search.py run_ray_search] Running ray search for spec reinforce_cartpole
NVIDIA drive version: 440.33.01 CUDA version: 10.2
Error logs
python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_baseline_cartpole search
[2020-01-30 11:38:57,177 PID:4355 INFO run_lab.py read_spec_and_run] Running lab spec_file:slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json spec_name:reinforce_baseline_cartpole in mode:search
[2020-01-30 11:38:57,183 PID:4355 INFO search.py run_ray_search] Running ray search for spec reinforce_baseline_cartpole
2020-01-30 11:38:57,183 WARNING worker.py:1341 -- WARNING: Not updating worker name since `setproctitle` is not installed. Install this with `pip install setproctitle` (or ray[debug]) to enable monitoring of worker processes.
2020-01-30 11:38:57,183 INFO node.py:497 -- Process STDOUT and STDERR is being redirected to /tmp/ray/session_2020-01-30_11-38-57_183527_4355/logs.
2020-01-30 11:38:57,288 INFO services.py:409 -- Waiting for redis server at 127.0.0.1:59003 to respond...
2020-01-30 11:38:57,409 INFO services.py:409 -- Waiting for redis server at 127.0.0.1:55931 to respond...
2020-01-30 11:38:57,414 INFO services.py:806 -- Starting Redis shard with 3.35 GB max memory.
2020-01-30 11:38:57,435 INFO node.py:511 -- Process STDOUT and STDERR is being redirected to /tmp/ray/session_2020-01-30_11-38-57_183527_4355/logs.
2020-01-30 11:38:57,435 INFO services.py:1441 -- Starting the Plasma object store with 5.02 GB memory using /dev/shm.
2020-01-30 11:38:57,543 INFO tune.py:60 -- Tip: to resume incomplete experiments, pass resume='prompt' or resume=True to run()
2020-01-30 11:38:57,543 INFO tune.py:223 -- Starting a new experiment.
== Status ==
Using FIFO scheduling algorithm.
Resources requested: 0/8 CPUs, 0/1 GPUs
Memory usage on this node: 2.1/16.7 GB
2020-01-30 11:38:57,572 WARNING logger.py:130 -- Couldn't import TensorFlow - disabling TensorBoard logging.
2020-01-30 11:38:57,573 WARNING logger.py:224 -- Could not instantiate <class 'ray.tune.logger.TFLogger'> - skipping.
== Status ==
Using FIFO scheduling algorithm.
Resources requested: 4/8 CPUs, 0/1 GPUs
Memory usage on this node: 2.2/16.7 GB
Result logdir: /home/joe/ray_results/reinforce_baseline_cartpole
Number of trials: 2 ({'RUNNING': 1, 'PENDING': 1})
PENDING trials:
- ray_trainable_1_agent.0.algorithm.center_return=False,trial_index=1: PENDING
RUNNING trials:
- ray_trainable_0_agent.0.algorithm.center_return=True,trial_index=0: RUNNING
2020-01-30 11:38:57,596 WARNING logger.py:130 -- Couldn't import TensorFlow - disabling TensorBoard logging.
2020-01-30 11:38:57,607 WARNING logger.py:224 -- Could not instantiate <class 'ray.tune.logger.TFLogger'> - skipping.
(pid=4389) [2020-01-30 11:38:58,297 PID:4389 INFO logger.py info] Running sessions
(pid=4388) [2020-01-30 11:38:58,292 PID:4388 INFO logger.py info] Running sessions
(pid=4388) terminate called after throwing an instance of 'c10::Error'
(pid=4388) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4388) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcf770dedc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4388) frame #1: <unknown function> + 0xca67 (0x7fcf6f2daa67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4388) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcf6f9fbb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4388) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcfa636128a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4388) frame #4: <unknown function> + 0xc8421 (0x7fcfbb3bd421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4388) frame #5: <unknown function> + 0x76db (0x7fcfc0c466db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4388) frame #6: clone + 0x3f (0x7fcfc096f88f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4388)
(pid=4388) Fatal Python error: Aborted
(pid=4388)
(pid=4388) Stack (most recent call first):
(pid=4389) [2020-01-30 11:38:58,326 PID:4456 INFO openai.py __init__] OpenAIEnv:
(pid=4389) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4389) - eval_frequency = 2000
(pid=4389) - log_frequency = 10000
(pid=4389) - frame_op = None
(pid=4389) - frame_op_len = None
(pid=4389) - image_downsize = (84, 84)
(pid=4389) - normalize_state = False
(pid=4389) - reward_scale = None
(pid=4389) - num_envs = 1
(pid=4389) - name = CartPole-v0
(pid=4389) - max_t = 200
(pid=4389) - max_frame = 100000
(pid=4389) - to_render = False
(pid=4389) - is_venv = False
(pid=4389) - clock_speed = 1
(pid=4389) - clock = <slm_lab.env.base.Clock object at 0x7fcc1a023d30>
(pid=4389) - done = False
(pid=4389) - total_reward = nan
(pid=4389) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4389) - observation_space = Box(4,)
(pid=4389) - action_space = Discrete(2)
(pid=4389) - observable_dim = {'state': 4}
(pid=4389) - action_dim = 2
(pid=4389) - is_discrete = True
(pid=4389) [2020-01-30 11:38:58,327 PID:4453 INFO openai.py __init__] OpenAIEnv:
(pid=4389) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4389) - eval_frequency = 2000
(pid=4389) - log_frequency = 10000
(pid=4389) - frame_op = None
(pid=4389) - frame_op_len = None
(pid=4389) - image_downsize = (84, 84)
(pid=4389) - normalize_state = False
(pid=4389) - reward_scale = None
(pid=4389) - num_envs = 1
(pid=4389) - name = CartPole-v0
(pid=4389) - max_t = 200
(pid=4389) - max_frame = 100000
(pid=4389) - to_render = False
(pid=4389) - is_venv = False
(pid=4389) - clock_speed = 1
(pid=4389) - clock = <slm_lab.env.base.Clock object at 0x7fcc1a023d30>
(pid=4389) - done = False
(pid=4389) - total_reward = nan
(pid=4389) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4389) - observation_space = Box(4,)
(pid=4389) - action_space = Discrete(2)
(pid=4389) - observable_dim = {'state': 4}
(pid=4389) - action_dim = 2
(pid=4389) - is_discrete = True
(pid=4389) [2020-01-30 11:38:58,328 PID:4450 INFO openai.py __init__] OpenAIEnv:
(pid=4389) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4389) - eval_frequency = 2000
(pid=4389) - log_frequency = 10000
(pid=4389) - frame_op = None
(pid=4389) - frame_op_len = None
(pid=4389) - image_downsize = (84, 84)
(pid=4389) - normalize_state = False
(pid=4389) - reward_scale = None
(pid=4389) - num_envs = 1
(pid=4389) - name = CartPole-v0
(pid=4389) - max_t = 200
(pid=4389) - max_frame = 100000
(pid=4389) - to_render = False
(pid=4389) - is_venv = False
(pid=4389) - clock_speed = 1
(pid=4389) - clock = <slm_lab.env.base.Clock object at 0x7fcc1a023d30>
(pid=4389) - done = False
(pid=4389) - total_reward = nan
(pid=4389) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4389) - observation_space = Box(4,)
(pid=4389) - action_space = Discrete(2)
(pid=4389) - observable_dim = {'state': 4}
(pid=4389) - action_dim = 2
(pid=4389) - is_discrete = True
(pid=4389) [2020-01-30 11:38:58,335 PID:4458 INFO openai.py __init__] OpenAIEnv:
(pid=4389) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4389) - eval_frequency = 2000
(pid=4389) - log_frequency = 10000
(pid=4389) - frame_op = None
(pid=4389) - frame_op_len = None
(pid=4389) - image_downsize = (84, 84)
(pid=4389) - normalize_state = False
(pid=4389) - reward_scale = None
(pid=4389) - num_envs = 1
(pid=4389) - name = CartPole-v0
(pid=4389) - max_t = 200
(pid=4389) - max_frame = 100000
(pid=4389) - to_render = False
(pid=4389) - is_venv = False
(pid=4389) - clock_speed = 1
(pid=4389) - clock = <slm_lab.env.base.Clock object at 0x7fcc1a023d30>
(pid=4389) - done = False
(pid=4389) - total_reward = nan
(pid=4389) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4389) - observation_space = Box(4,)
(pid=4389) - action_space = Discrete(2)
(pid=4389) - observable_dim = {'state': 4}
(pid=4389) - action_dim = 2
(pid=4389) - is_discrete = True
(pid=4388) [2020-01-30 11:38:58,313 PID:4440 INFO openai.py __init__] OpenAIEnv:
(pid=4388) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4388) - eval_frequency = 2000
(pid=4388) - log_frequency = 10000
(pid=4388) - frame_op = None
(pid=4388) - frame_op_len = None
(pid=4388) - image_downsize = (84, 84)
(pid=4388) - normalize_state = False
(pid=4388) - reward_scale = None
(pid=4388) - num_envs = 1
(pid=4388) - name = CartPole-v0
(pid=4388) - max_t = 200
(pid=4388) - max_frame = 100000
(pid=4388) - to_render = False
(pid=4388) - is_venv = False
(pid=4388) - clock_speed = 1
(pid=4388) - clock = <slm_lab.env.base.Clock object at 0x7fce28f7fcf8>
(pid=4388) - done = False
(pid=4388) - total_reward = nan
(pid=4388) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4388) - observation_space = Box(4,)
(pid=4388) - action_space = Discrete(2)
(pid=4388) - observable_dim = {'state': 4}
(pid=4388) - action_dim = 2
(pid=4388) - is_discrete = True
(pid=4388) [2020-01-30 11:38:58,318 PID:4445 INFO openai.py __init__] OpenAIEnv:
(pid=4388) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4388) - eval_frequency = 2000
(pid=4388) - log_frequency = 10000
(pid=4388) - frame_op = None
(pid=4388) - frame_op_len = None
(pid=4388) - image_downsize = (84, 84)
(pid=4388) - normalize_state = False
(pid=4388) - reward_scale = None
(pid=4388) - num_envs = 1
(pid=4388) - name = CartPole-v0
(pid=4388) - max_t = 200
(pid=4388) - max_frame = 100000
(pid=4388) - to_render = False
(pid=4388) - is_venv = False
(pid=4388) - clock_speed = 1
(pid=4388) - clock = <slm_lab.env.base.Clock object at 0x7fce28f7fcf8>
(pid=4388) - done = False
(pid=4388) - total_reward = nan
(pid=4388) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4388) - observation_space = Box(4,)
(pid=4388) - action_space = Discrete(2)
(pid=4388) - observable_dim = {'state': 4}
(pid=4388) - action_dim = 2
(pid=4388) - is_discrete = True
(pid=4388) [2020-01-30 11:38:58,319 PID:4449 INFO openai.py __init__] OpenAIEnv:
(pid=4388) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4388) - eval_frequency = 2000
(pid=4388) - log_frequency = 10000
(pid=4388) - frame_op = None
(pid=4388) - frame_op_len = None
(pid=4388) - image_downsize = (84, 84)
(pid=4388) - normalize_state = False
(pid=4388) - reward_scale = None
(pid=4388) - num_envs = 1
(pid=4388) - name = CartPole-v0
(pid=4388) - max_t = 200
(pid=4388) - max_frame = 100000
(pid=4388) - to_render = False
(pid=4388) - is_venv = False
(pid=4388) - clock_speed = 1
(pid=4388) - clock = <slm_lab.env.base.Clock object at 0x7fce28f7fcf8>
(pid=4388) - done = False
(pid=4388) - total_reward = nan
(pid=4388) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4388) - observation_space = Box(4,)
(pid=4388) - action_space = Discrete(2)
(pid=4388) - observable_dim = {'state': 4}
(pid=4388) - action_dim = 2
(pid=4388) - is_discrete = True
(pid=4388) [2020-01-30 11:38:58,323 PID:4452 INFO openai.py __init__] OpenAIEnv:
(pid=4388) - env_spec = {'max_frame': 100000, 'max_t': None, 'name': 'CartPole-v0'}
(pid=4388) - eval_frequency = 2000
(pid=4388) - log_frequency = 10000
(pid=4388) - frame_op = None
(pid=4388) - frame_op_len = None
(pid=4388) - image_downsize = (84, 84)
(pid=4388) - normalize_state = False
(pid=4388) - reward_scale = None
(pid=4388) - num_envs = 1
(pid=4388) - name = CartPole-v0
(pid=4388) - max_t = 200
(pid=4388) - max_frame = 100000
(pid=4388) - to_render = False
(pid=4388) - is_venv = False
(pid=4388) - clock_speed = 1
(pid=4388) - clock = <slm_lab.env.base.Clock object at 0x7fce28f7fcf8>
(pid=4388) - done = False
(pid=4388) - total_reward = nan
(pid=4388) - u_env = <TrackReward<TimeLimit<CartPoleEnv<CartPole-v0>>>>
(pid=4388) - observation_space = Box(4,)
(pid=4388) - action_space = Discrete(2)
(pid=4388) - observable_dim = {'state': 4}
(pid=4388) - action_dim = 2
(pid=4388) - is_discrete = True
(pid=4389) [2020-01-30 11:38:58,339 PID:4453 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search
(pid=4389) [2020-01-30 11:38:58,340 PID:4450 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search
(pid=4389) [2020-01-30 11:38:58,343 PID:4456 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search
(pid=4389) [2020-01-30 11:38:58,345 PID:4450 INFO base.py __init__][2020-01-30 11:38:58,345 PID:4453 INFO base.py __init__] Reinforce:
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bddcc0>
(pid=4389) - algorithm_spec = {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1}
(pid=4389) - name = Reinforce
(pid=4389) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4389) - net_spec = {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bddcc0>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56cc0>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bcb710>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10bddd68>"
(pid=4389) }
(pid=4389) - action_pdtype = default
(pid=4389) - action_policy = <function default at 0x7fcc21560620>
(pid=4389) - center_return = False
(pid=4389) - explore_var_spec = None
(pid=4389) - entropy_coef_spec = {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01}
(pid=4389) - policy_loss_coef = 1.0
(pid=4389) - gamma = 0.99
(pid=4389) - training_frequency = 1
(pid=4389) - to_train = 0
(pid=4389) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10bddd30>
(pid=4389) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10bdda20>
(pid=4389) - net = MLPNet(
(pid=4389) (model): Sequential(
(pid=4389) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4389) (1): SELU()
(pid=4389) )
(pid=4389) (model_tail): Sequential(
(pid=4389) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4389) )
(pid=4389) (loss_fn): MSELoss()
(pid=4389) )
(pid=4389) - net_names = ['net']
(pid=4389) - optim = Adam (
(pid=4389) Parameter Group 0
(pid=4389) amsgrad: False
(pid=4389) betas: (0.9, 0.999)
(pid=4389) eps: 1e-08
(pid=4389) lr: 0.002
(pid=4389) weight_decay: 0
(pid=4389) )
(pid=4389) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fcc10ba20b8>
(pid=4389) - global_net = None
(pid=4389) Reinforce:
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bdddd8>
(pid=4389) - algorithm_spec = {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) [2020-01-30 11:38:58,330 PID:4445 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search
(pid=4388) [2020-01-30 11:38:58,330 PID:4449 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search
(pid=4388) [2020-01-30 11:38:58,335 PID:4452 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search
(pid=4388) [2020-01-30 11:38:58,335 PID:4449 INFO base.py __init__] Reinforce:
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e097f60>
(pid=4388) - algorithm_spec = {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1}
(pid=4388) - name = Reinforce
(pid=4388) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4388) - net_spec = {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e097f60>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044eb8>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e097fd0>"
(pid=4388) }
(pid=4388) - action_pdtype = default
(pid=4388) - action_policy = <function default at 0x7fce304ad620>
(pid=4388) - center_return = True
(pid=4388) - explore_var_spec = None
(pid=4388) - entropy_coef_spec = {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01}
(pid=4388) - policy_loss_coef = 1.0
(pid=4388) - gamma = 0.99
(pid=4388) - training_frequency = 1
(pid=4388) - to_train = 0
(pid=4388) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e097c88>
(pid=4388) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e083940>
(pid=4388) - net = MLPNet(
(pid=4388) (model): Sequential(
(pid=4388) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4388) (1): SELU()
(pid=4388) )
(pid=4388) (model_tail): Sequential(
(pid=4388) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4388) )
(pid=4388) (loss_fn): MSELoss()
(pid=4388) )
(pid=4388) - net_names = ['net']
(pid=4388) - optim = Adam (
(pid=4388) Parameter Group 0
(pid=4388) amsgrad: False
(pid=4388) betas: (0.9, 0.999)
(pid=4388) eps: 1e-08
(pid=4388) lr: 0.002
(pid=4388) weight_decay: 0
(pid=4388) )
(pid=4388) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fce0e0562e8>
(pid=4388) - global_net = None
(pid=4388) [2020-01-30 11:38:58,335 PID:4445 INFO base.py __init__] Reinforce:
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e098da0>
(pid=4388) - algorithm_spec = {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1}
(pid=4389) - name = Reinforce
(pid=4389) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4389) - net_spec = {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bdddd8>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56da0>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bc5828>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10bdde80>"
(pid=4389) }
(pid=4389) - action_pdtype = default
(pid=4389) - action_policy = <function default at 0x7fcc21560620>
(pid=4389) - center_return = False
(pid=4389) - explore_var_spec = None
(pid=4389) - entropy_coef_spec = {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01}
(pid=4389) - policy_loss_coef = 1.0
(pid=4389) - gamma = 0.99
(pid=4389) - training_frequency = 1
(pid=4389) - to_train = 0
(pid=4389) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10bdde48>
(pid=4389) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10bddb38>
(pid=4389) - net = MLPNet(
(pid=4389) (model): Sequential(
(pid=4389) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4389) (1): SELU()
(pid=4389) )
(pid=4389) (model_tail): Sequential(
(pid=4389) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4389) )
(pid=4389) (loss_fn): MSELoss()
(pid=4389) )
(pid=4389) - net_names = ['net']
(pid=4389) - optim = Adam (
(pid=4389) Parameter Group 0
(pid=4389) amsgrad: False
(pid=4389) betas: (0.9, 0.999)
(pid=4389) eps: 1e-08
(pid=4389) lr: 0.002
(pid=4389) weight_decay: 0
(pid=4389) )
(pid=4389) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fcc10ba11d0>
(pid=4389) - global_net = None
(pid=4389) [2020-01-30 11:38:58,347 PID:4453 INFO __init__.py __init__][2020-01-30 11:38:58,347 PID:4450 INFO __init__.py __init__] Agent:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1},
(pid=4389) 'memory': {'name': 'OnPolicyReplay'},
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1}
(pid=4388) - name = Reinforce
(pid=4388) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4388) - net_spec = {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e098da0>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044da0>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e098e48>"
(pid=4388) }
(pid=4388) - action_pdtype = default
(pid=4388) - action_policy = <function default at 0x7fce304ad620>
(pid=4388) - center_return = True
(pid=4388) - explore_var_spec = None
(pid=4388) - entropy_coef_spec = {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01}
(pid=4388) - policy_loss_coef = 1.0
(pid=4388) - gamma = 0.99
(pid=4388) - training_frequency = 1
(pid=4388) - to_train = 0
(pid=4388) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e098e10>
(pid=4388) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e098f28>
(pid=4388) - net = MLPNet(
(pid=4388) (model): Sequential(
(pid=4388) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4388) (1): SELU()
(pid=4388) )
(pid=4388) (model_tail): Sequential(
(pid=4388) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4388) )
(pid=4388) (loss_fn): MSELoss()
(pid=4388) )
(pid=4388) - net_names = ['net']
(pid=4388) - optim = Adam (
(pid=4388) Parameter Group 0
(pid=4388) amsgrad: False
(pid=4388) betas: (0.9, 0.999)
(pid=4388) eps: 1e-08
(pid=4388) lr: 0.002
(pid=4388) weight_decay: 0
(pid=4388) )
(pid=4388) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fce0e05b1d0>
(pid=4388) - global_net = None
(pid=4388) [2020-01-30 11:38:58,336 PID:4449 INFO __init__.py __init__] Agent:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1},
(pid=4388) 'memory': {'name': 'OnPolicyReplay'},
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'net': {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}}
(pid=4389) - name = Reinforce
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bdddd8>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56da0>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bc5828>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10bdde80>"
(pid=4389) }
(pid=4389) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fcc10bdde10>
(pid=4389) Agent:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1},
(pid=4389) 'memory': {'name': 'OnPolicyReplay'},
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'net': {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}}
(pid=4389) - name = Reinforce
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bddcc0>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56cc0>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bcb710>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10bddd68>"
(pid=4389) }
(pid=4389) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fcc10bddcf8>
(pid=4389) [2020-01-30 11:38:58,347 PID:4458 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search[2020-01-30 11:38:58,347 PID:4450 INFO logger.py info][2020-01-30 11:38:58,347 PID:4453 INFO logger.py info]
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'net': {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}}
(pid=4388) - name = Reinforce
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e097f60>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044eb8>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e097fd0>"
(pid=4388) }
(pid=4388) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fce0e097f98>
(pid=4388) [2020-01-30 11:38:58,337 PID:4449 INFO logger.py info] Session:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - index = 2
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e097f60>
(pid=4388) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044eb8>
(pid=4388) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044eb8>
(pid=4388) [2020-01-30 11:38:58,337 PID:4449 INFO logger.py info] Running RL loop for trial 0 session 2
(pid=4388) [2020-01-30 11:38:58,337 PID:4445 INFO __init__.py __init__] Agent:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1},
(pid=4388) 'memory': {'name': 'OnPolicyReplay'},
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'net': {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}}
(pid=4388) - name = Reinforce
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e098da0>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044da0>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4389) Session:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - index = 0
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bddcc0>
(pid=4389) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56cc0>
(pid=4389) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56cc0> Session:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - index = 1
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bdddd8>
(pid=4389) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56da0>
(pid=4389) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56da0>
(pid=4389)
(pid=4389) [2020-01-30 11:38:58,347 PID:4450 INFO logger.py info] Running RL loop for trial 1 session 0[2020-01-30 11:38:58,347 PID:4453 INFO logger.py info]
(pid=4389) Running RL loop for trial 1 session 1
(pid=4389) [2020-01-30 11:38:58,348 PID:4456 INFO base.py __init__] Reinforce:
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bdcf28>
(pid=4389) - algorithm_spec = {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1}
(pid=4389) - name = Reinforce
(pid=4389) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4389) - net_spec = {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bdcf28>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56eb8>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bc7940>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10bdcfd0>"
(pid=4389) }
(pid=4389) - action_pdtype = default
(pid=4389) - action_policy = <function default at 0x7fcc21560620>
(pid=4389) - center_return = False
(pid=4389) - explore_var_spec = None
(pid=4389) - entropy_coef_spec = {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01}
(pid=4389) - policy_loss_coef = 1.0
(pid=4389) - gamma = 0.99
(pid=4389) - training_frequency = 1
(pid=4389) - to_train = 0
(pid=4389) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10bdcf98>
(pid=4389) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10bdcc50>
(pid=4389) - net = MLPNet(
(pid=4389) (model): Sequential(
(pid=4389) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4389) (1): SELU()
(pid=4389) )
(pid=4389) (model_tail): Sequential(
(pid=4389) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4389) )
(pid=4389) (loss_fn): MSELoss()
(pid=4389) )
(pid=4389) - net_names = ['net']
(pid=4389) - optim = Adam (
(pid=4389) Parameter Group 0
(pid=4389) amsgrad: False
(pid=4389) betas: (0.9, 0.999)
(pid=4389) eps: 1e-08
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e098e48>"
(pid=4388) }
(pid=4388) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fce0e098dd8>
(pid=4388) [2020-01-30 11:38:58,338 PID:4445 INFO logger.py info] Session:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - index = 1
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e098da0>
(pid=4388) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044da0>
(pid=4388) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044da0>
(pid=4388) [2020-01-30 11:38:58,338 PID:4445 INFO logger.py info] Running RL loop for trial 0 session 1
(pid=4388) [2020-01-30 11:38:58,340 PID:4449 INFO __init__.py log_summary] Trial 0 session 2 reinforce_baseline_cartpole_t0_s2 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4388) [2020-01-30 11:38:58,340 PID:4452 INFO base.py __init__] Reinforce:
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e082a58>
(pid=4388) - algorithm_spec = {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1}
(pid=4388) - name = Reinforce
(pid=4388) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4388) - net_spec = {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e082a58>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044fd0>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e0540b8>"
(pid=4388) }
(pid=4388) - action_pdtype = default
(pid=4388) - action_policy = <function default at 0x7fce304ad620>
(pid=4388) - center_return = True
(pid=4388) - explore_var_spec = None
(pid=4388) - entropy_coef_spec = {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01}
(pid=4388) - policy_loss_coef = 1.0
(pid=4388) - gamma = 0.99
(pid=4388) - training_frequency = 1
(pid=4388) - to_train = 0
(pid=4388) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e054080>
(pid=4388) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e054160>
(pid=4388) - net = MLPNet(
(pid=4388) (model): Sequential(
(pid=4388) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4388) (1): SELU()
(pid=4388) )
(pid=4388) (model_tail): Sequential(
(pid=4388) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4388) )
(pid=4388) (loss_fn): MSELoss()
(pid=4388) )
(pid=4388) - net_names = ['net']
(pid=4388) - optim = Adam (
(pid=4388) Parameter Group 0
(pid=4388) amsgrad: False
(pid=4388) betas: (0.9, 0.999)
(pid=4388) eps: 1e-08
(pid=4389) lr: 0.002
(pid=4389) weight_decay: 0
(pid=4389) )
(pid=4389) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fcc10b9a2e8>
(pid=4389) - global_net = None
(pid=4389) [2020-01-30 11:38:58,350 PID:4456 INFO __init__.py __init__] Agent:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1},
(pid=4389) 'memory': {'name': 'OnPolicyReplay'},
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'net': {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}}
(pid=4389) - name = Reinforce
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bdcf28>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56eb8>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bc7940>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10bdcfd0>"
(pid=4389) }
(pid=4389) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fcc10bdcf60>
(pid=4389) [2020-01-30 11:38:58,351 PID:4456 INFO logger.py info] Session:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - index = 2
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bdcf28>
(pid=4389) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56eb8>
(pid=4389) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56eb8>
(pid=4389) [2020-01-30 11:38:58,351 PID:4456 INFO logger.py info] Running RL loop for trial 1 session 2
(pid=4389) [2020-01-30 11:38:58,351 PID:4450 INFO __init__.py log_summary] Trial 1 session 0 reinforce_baseline_cartpole_t1_s0 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4389) [2020-01-30 11:38:58,351 PID:4453 INFO __init__.py log_summary] Trial 1 session 1 reinforce_baseline_cartpole_t1_s1 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4389) [2020-01-30 11:38:58,352 PID:4458 INFO base.py __init__] Reinforce:
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bddd68>
(pid=4389) - algorithm_spec = {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1}
(pid=4389) - name = Reinforce
(pid=4389) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4389) - net_spec = {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bddd68>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56fd0>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4388) lr: 0.002
(pid=4388) weight_decay: 0
(pid=4388) )
(pid=4388) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fce0e054400>
(pid=4388) - global_net = None
(pid=4388) [2020-01-30 11:38:58,342 PID:4445 INFO __init__.py log_summary] Trial 0 session 1 reinforce_baseline_cartpole_t0_s1 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4388) [2020-01-30 11:38:58,342 PID:4452 INFO __init__.py __init__] Agent:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1},
(pid=4388) 'memory': {'name': 'OnPolicyReplay'},
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'net': {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}}
(pid=4388) - name = Reinforce
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e082a58>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044fd0>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e0540b8>"
(pid=4388) }
(pid=4388) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fce0e054048>
(pid=4388) [2020-01-30 11:38:58,342 PID:4452 INFO logger.py info] Session:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - index = 3
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e082a58>
(pid=4388) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044fd0>
(pid=4388) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044fd0>
(pid=4388) [2020-01-30 11:38:58,342 PID:4452 INFO logger.py info] Running RL loop for trial 0 session 3
(pid=4388) [2020-01-30 11:38:58,343 PID:4440 INFO base.py post_init_nets] Initialized algorithm models for lab_mode: search
(pid=4388) [2020-01-30 11:38:58,346 PID:4452 INFO __init__.py log_summary] Trial 0 session 3 reinforce_baseline_cartpole_t0_s3 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4388) [2020-01-30 11:38:58,348 PID:4440 INFO base.py __init__] Reinforce:
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e09ac88>
(pid=4388) - algorithm_spec = {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1}
(pid=4388) - name = Reinforce
(pid=4388) - memory_spec = {'name': 'OnPolicyReplay'}
(pid=4388) - net_spec = {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e09ac88>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044cc0>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) terminate called after throwing an instance of 'c10::Error'
(pid=4388) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4388) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcf770dedc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4388) frame #1: <unknown function> + 0xca67 (0x7fcf6f2daa67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4388) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcf6f9fbb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4388) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcfa636128a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4388) frame #4: <unknown function> + 0xc8421 (0x7fcfbb3bd421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4388) frame #5: <unknown function> + 0x76db (0x7fcfc0c466db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4388) frame #6: clone + 0x3f (0x7fcfc096f88f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4388)
(pid=4388) Fatal Python error: Aborted
(pid=4388)
(pid=4388) Stack (most recent call first):
(pid=4388) terminate called after throwing an instance of 'c10::Error'
(pid=4388) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4388) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcf770dedc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4388) frame #1: <unknown function> + 0xca67 (0x7fcf6f2daa67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4388) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcf6f9fbb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4388) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcfa636128a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4388) frame #4: <unknown function> + 0xc8421 (0x7fcfbb3bd421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4388) frame #5: <unknown function> + 0x76db (0x7fcfc0c466db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4388) frame #6: clone + 0x3f (0x7fcfc096f88f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4388)
(pid=4388) Fatal Python error: Aborted
(pid=4388)
(pid=4388) Stack (most recent call first):
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bc6a58>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10b9a0b8>"
(pid=4389) }
(pid=4389) - action_pdtype = default
(pid=4389) - action_policy = <function default at 0x7fcc21560620>
(pid=4389) - center_return = False
(pid=4389) - explore_var_spec = None
(pid=4389) - entropy_coef_spec = {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01}
(pid=4389) - policy_loss_coef = 1.0
(pid=4389) - gamma = 0.99
(pid=4389) - training_frequency = 1
(pid=4389) - to_train = 0
(pid=4389) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10b9a080>
(pid=4389) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fcc10b9a160>
(pid=4389) - net = MLPNet(
(pid=4389) (model): Sequential(
(pid=4389) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4389) (1): SELU()
(pid=4389) )
(pid=4389) (model_tail): Sequential(
(pid=4389) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4389) )
(pid=4389) (loss_fn): MSELoss()
(pid=4389) )
(pid=4389) - net_names = ['net']
(pid=4389) - optim = Adam (
(pid=4389) Parameter Group 0
(pid=4389) amsgrad: False
(pid=4389) betas: (0.9, 0.999)
(pid=4389) eps: 1e-08
(pid=4389) lr: 0.002
(pid=4389) weight_decay: 0
(pid=4389) )
(pid=4389) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fcc10b9a400>
(pid=4389) - global_net = None
(pid=4389) [2020-01-30 11:38:58,354 PID:4458 INFO __init__.py __init__] Agent:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4389) 'action_policy': 'default',
(pid=4389) 'center_return': False,
(pid=4389) 'entropy_coef_spec': {'end_step': 20000,
(pid=4389) 'end_val': 0.001,
(pid=4389) 'name': 'linear_decay',
(pid=4389) 'start_step': 0,
(pid=4389) 'start_val': 0.01},
(pid=4389) 'explore_var_spec': None,
(pid=4389) 'gamma': 0.99,
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'training_frequency': 1},
(pid=4389) 'memory': {'name': 'OnPolicyReplay'},
(pid=4389) 'name': 'Reinforce',
(pid=4389) 'net': {'clip_grad_val': None,
(pid=4389) 'hid_layers': [64],
(pid=4389) 'hid_layers_activation': 'selu',
(pid=4389) 'loss_spec': {'name': 'MSELoss'},
(pid=4389) 'lr_scheduler_spec': None,
(pid=4389) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4389) 'type': 'MLPNet'}}
(pid=4389) - name = Reinforce
(pid=4389) - body = body: {
(pid=4389) "agent": "<slm_lab.agent.Agent object at 0x7fcc10bddd68>",
(pid=4389) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56fd0>",
(pid=4389) "a": 0,
(pid=4389) "e": 0,
(pid=4389) "b": 0,
(pid=4389) "aeb": "(0, 0, 0)",
(pid=4389) "explore_var": NaN,
(pid=4389) "entropy_coef": 0.01,
(pid=4389) "loss": NaN,
(pid=4389) "mean_entropy": NaN,
(pid=4389) "mean_grad_norm": NaN,
(pid=4389) "best_total_reward_ma": -Infinity,
(pid=4389) "total_reward_ma": NaN,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e09ad30>"
(pid=4388) }
(pid=4388) - action_pdtype = default
(pid=4388) - action_policy = <function default at 0x7fce304ad620>
(pid=4388) - center_return = True
(pid=4388) - explore_var_spec = None
(pid=4388) - entropy_coef_spec = {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01}
(pid=4388) - policy_loss_coef = 1.0
(pid=4388) - gamma = 0.99
(pid=4388) - training_frequency = 1
(pid=4388) - to_train = 0
(pid=4388) - explore_var_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e09acf8>
(pid=4388) - entropy_coef_scheduler = <slm_lab.agent.algorithm.policy_util.VarScheduler object at 0x7fce0e09ae10>
(pid=4388) - net = MLPNet(
(pid=4388) (model): Sequential(
(pid=4388) (0): Linear(in_features=4, out_features=64, bias=True)
(pid=4388) (1): SELU()
(pid=4388) )
(pid=4388) (model_tail): Sequential(
(pid=4388) (0): Linear(in_features=64, out_features=2, bias=True)
(pid=4388) )
(pid=4388) (loss_fn): MSELoss()
(pid=4388) )
(pid=4388) - net_names = ['net']
(pid=4388) - optim = Adam (
(pid=4388) Parameter Group 0
(pid=4388) amsgrad: False
(pid=4388) betas: (0.9, 0.999)
(pid=4388) eps: 1e-08
(pid=4388) lr: 0.002
(pid=4388) weight_decay: 0
(pid=4388) )
(pid=4388) - lr_scheduler = <slm_lab.agent.net.net_util.NoOpLRScheduler object at 0x7fce0e05c0b8>
(pid=4388) - global_net = None
(pid=4388) [2020-01-30 11:38:58,350 PID:4440 INFO __init__.py __init__] Agent:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - agent_spec = {'algorithm': {'action_pdtype': 'default',
(pid=4388) 'action_policy': 'default',
(pid=4388) 'center_return': True,
(pid=4388) 'entropy_coef_spec': {'end_step': 20000,
(pid=4388) 'end_val': 0.001,
(pid=4388) 'name': 'linear_decay',
(pid=4388) 'start_step': 0,
(pid=4388) 'start_val': 0.01},
(pid=4388) 'explore_var_spec': None,
(pid=4388) 'gamma': 0.99,
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'training_frequency': 1},
(pid=4388) 'memory': {'name': 'OnPolicyReplay'},
(pid=4388) 'name': 'Reinforce',
(pid=4388) 'net': {'clip_grad_val': None,
(pid=4388) 'hid_layers': [64],
(pid=4388) 'hid_layers_activation': 'selu',
(pid=4388) 'loss_spec': {'name': 'MSELoss'},
(pid=4388) 'lr_scheduler_spec': None,
(pid=4388) 'optim_spec': {'lr': 0.002, 'name': 'Adam'},
(pid=4388) 'type': 'MLPNet'}}
(pid=4388) - name = Reinforce
(pid=4388) - body = body: {
(pid=4388) "agent": "<slm_lab.agent.Agent object at 0x7fce0e09ac88>",
(pid=4388) "env": "<slm_lab.env.openai.OpenAIEnv object at 0x7fce28044cc0>",
(pid=4388) "a": 0,
(pid=4388) "e": 0,
(pid=4388) "b": 0,
(pid=4388) "aeb": "(0, 0, 0)",
(pid=4388) "explore_var": NaN,
(pid=4388) "entropy_coef": 0.01,
(pid=4388) "loss": NaN,
(pid=4388) "mean_entropy": NaN,
(pid=4388) "mean_grad_norm": NaN,
(pid=4388) "best_total_reward_ma": -Infinity,
(pid=4389) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4389) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fcc10bc6a58>",
(pid=4389) "tb_actions": [],
(pid=4389) "tb_tracker": {},
(pid=4389) "observation_space": "Box(4,)",
(pid=4389) "action_space": "Discrete(2)",
(pid=4389) "observable_dim": {
(pid=4389) "state": 4
(pid=4389) },
(pid=4389) "state_dim": 4,
(pid=4389) "action_dim": 2,
(pid=4389) "is_discrete": true,
(pid=4389) "action_type": "discrete",
(pid=4389) "action_pdtype": "Categorical",
(pid=4389) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4389) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fcc10b9a0b8>"
(pid=4389) }
(pid=4389) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fcc10b9a048>
(pid=4389) [2020-01-30 11:38:58,354 PID:4458 INFO logger.py info] Session:
(pid=4389) - spec = reinforce_baseline_cartpole
(pid=4389) - index = 3
(pid=4389) - agent = <slm_lab.agent.Agent object at 0x7fcc10bddd68>
(pid=4389) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56fd0>
(pid=4389) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fcc10c56fd0>
(pid=4389) [2020-01-30 11:38:58,354 PID:4458 INFO logger.py info] Running RL loop for trial 1 session 3
(pid=4389) [2020-01-30 11:38:58,355 PID:4456 INFO __init__.py log_summary] Trial 1 session 2 reinforce_baseline_cartpole_t1_s2 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4389) [2020-01-30 11:38:58,358 PID:4458 INFO __init__.py log_summary] Trial 1 session 3 reinforce_baseline_cartpole_t1_s3 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4388) "total_reward_ma": NaN,
(pid=4388) "train_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "eval_df": "Empty DataFrame\nColumns: [epi, t, wall_t, opt_step, frame, fps, total_reward, total_reward_ma, loss, lr, explore_var, entropy_coef, entropy, grad_norm]\nIndex: []",
(pid=4388) "tb_writer": "<torch.utils.tensorboard.writer.SummaryWriter object at 0x7fce2b00a780>",
(pid=4388) "tb_actions": [],
(pid=4388) "tb_tracker": {},
(pid=4388) "observation_space": "Box(4,)",
(pid=4388) "action_space": "Discrete(2)",
(pid=4388) "observable_dim": {
(pid=4388) "state": 4
(pid=4388) },
(pid=4388) "state_dim": 4,
(pid=4388) "action_dim": 2,
(pid=4388) "is_discrete": true,
(pid=4388) "action_type": "discrete",
(pid=4388) "action_pdtype": "Categorical",
(pid=4388) "ActionPD": "<class 'torch.distributions.categorical.Categorical'>",
(pid=4388) "memory": "<slm_lab.agent.memory.onpolicy.OnPolicyReplay object at 0x7fce0e09ad30>"
(pid=4388) }
(pid=4388) - algorithm = <slm_lab.agent.algorithm.reinforce.Reinforce object at 0x7fce0e09acc0>
(pid=4388) [2020-01-30 11:38:58,350 PID:4440 INFO logger.py info] Session:
(pid=4388) - spec = reinforce_baseline_cartpole
(pid=4388) - index = 0
(pid=4388) - agent = <slm_lab.agent.Agent object at 0x7fce0e09ac88>
(pid=4388) - env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044cc0>
(pid=4388) - eval_env = <slm_lab.env.openai.OpenAIEnv object at 0x7fce28044cc0>
(pid=4388) [2020-01-30 11:38:58,350 PID:4440 INFO logger.py info] Running RL loop for trial 0 session 0
(pid=4388) [2020-01-30 11:38:58,354 PID:4440 INFO __init__.py log_summary] Trial 0 session 0 reinforce_baseline_cartpole_t0_s0 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.002 explore_var: nan entropy_coef: 0.01 entropy: nan grad_norm: nan
(pid=4388) terminate called after throwing an instance of 'c10::Error'
(pid=4388) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4388) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcf770dedc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4388) frame #1: <unknown function> + 0xca67 (0x7fcf6f2daa67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4388) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcf6f9fbb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4388) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcfa636128a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4388) frame #4: <unknown function> + 0xc8421 (0x7fcfbb3bd421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4388) frame #5: <unknown function> + 0x76db (0x7fcfc0c466db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4388) frame #6: clone + 0x3f (0x7fcfc096f88f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4388)
(pid=4388) Fatal Python error: Aborted
(pid=4388)
(pid=4388) Stack (most recent call first):
(pid=4389) terminate called after throwing an instance of 'c10::Error'
(pid=4389) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4389) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcd68190dc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4389) frame #1: <unknown function> + 0xca67 (0x7fcd6038ca67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4389) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcd60aadb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4389) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcd9741328a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4389) frame #4: <unknown function> + 0xc8421 (0x7fcdac471421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4389) frame #5: <unknown function> + 0x76db (0x7fcdb1cfa6db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4389) frame #6: clone + 0x3f (0x7fcdb1a2388f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4389)
(pid=4389) Fatal Python error: Aborted
(pid=4389)
(pid=4389) Stack (most recent call first):
(pid=4389) terminate called after throwing an instance of 'c10::Error'
(pid=4389) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4389) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcd68190dc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4389) frame #1: <unknown function> + 0xca67 (0x7fcd6038ca67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4389) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcd60aadb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4389) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcd9741328a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4389) frame #4: <unknown function> + 0xc8421 (0x7fcdac471421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4389) frame #5: <unknown function> + 0x76db (0x7fcdb1cfa6db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4389) frame #6: clone + 0x3f (0x7fcdb1a2388f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4389)
(pid=4389) Fatal Python error: Aborted
(pid=4389)
(pid=4389) Stack (most recent call first):
(pid=4389) terminate called after throwing an instance of 'c10::Error'
(pid=4389) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4389) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcd68190dc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4389) frame #1: <unknown function> + 0xca67 (0x7fcd6038ca67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4389) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcd60aadb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4389) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcd9741328a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4389) frame #4: <unknown function> + 0xc8421 (0x7fcdac471421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4389) frame #5: <unknown function> + 0x76db (0x7fcdb1cfa6db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4389) frame #6: clone + 0x3f (0x7fcdb1a2388f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4389)
(pid=4389) Fatal Python error: Aborted
(pid=4389)
(pid=4389) Stack (most recent call first):
(pid=4389) terminate called after throwing an instance of 'c10::Error'
(pid=4389) what(): CUDA error: initialization error (getDevice at /opt/conda/conda-bld/pytorch_1556653114079/work/c10/cuda/impl/CUDAGuardImpl.h:35)
(pid=4389) frame #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x45 (0x7fcd68190dc5 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10.so)
(pid=4389) frame #1: <unknown function> + 0xca67 (0x7fcd6038ca67 in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libc10_cuda.so)
(pid=4389) frame #2: torch::autograd::Engine::thread_init(int) + 0x3ee (0x7fcd60aadb1e in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch.so.1)
(pid=4389) frame #3: torch::autograd::python::PythonEngine::thread_init(int) + 0x2a (0x7fcd9741328a in /home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
(pid=4389) frame #4: <unknown function> + 0xc8421 (0x7fcdac471421 in /home/joe/anaconda3/envs/lab/bin/../lib/libstdc++.so.6)
(pid=4389) frame #5: <unknown function> + 0x76db (0x7fcdb1cfa6db in /lib/x86_64-linux-gnu/libpthread.so.0)
(pid=4389) frame #6: clone + 0x3f (0x7fcdb1a2388f in /lib/x86_64-linux-gnu/libc.so.6)
(pid=4389)
(pid=4389) Fatal Python error: Aborted
(pid=4389)
(pid=4389) Stack (most recent call first):
(pid=4388) 2020-01-30 11:38:58,550 ERROR function_runner.py:96 -- Runner Thread raised error.
(pid=4388) Traceback (most recent call last):
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 90, in run
(pid=4388) self._entrypoint()
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 141, in entrypoint
(pid=4388) return self._trainable_func(config, self._status_reporter)
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 249, in _trainable_func
(pid=4388) output = train_func(config, reporter)
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/search.py", line 90, in ray_trainable
(pid=4388) metrics = Trial(spec).run()
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/control.py", line 181, in run
(pid=4388) metrics = analysis.analyze_trial(self.spec, session_metrics_list)
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 265, in analyze_trial
(pid=4388) trial_metrics = calc_trial_metrics(session_metrics_list, info_prepath)
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 187, in calc_trial_metrics
(pid=4388) frames = session_metrics_list[0]['local']['frames']
(pid=4388) IndexError: list index out of range
(pid=4388) Exception in thread Thread-1:
(pid=4388) Traceback (most recent call last):
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 90, in run
(pid=4388) self._entrypoint()
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 141, in entrypoint
(pid=4388) return self._trainable_func(config, self._status_reporter)
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 249, in _trainable_func
(pid=4388) output = train_func(config, reporter)
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/search.py", line 90, in ray_trainable
(pid=4388) metrics = Trial(spec).run()
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/control.py", line 181, in run
(pid=4388) metrics = analysis.analyze_trial(self.spec, session_metrics_list)
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 265, in analyze_trial
(pid=4388) trial_metrics = calc_trial_metrics(session_metrics_list, info_prepath)
(pid=4388) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 187, in calc_trial_metrics
(pid=4388) frames = session_metrics_list[0]['local']['frames']
(pid=4388) IndexError: list index out of range
(pid=4388)
(pid=4388) During handling of the above exception, another exception occurred:
(pid=4388)
(pid=4388) Traceback (most recent call last):
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/threading.py", line 917, in _bootstrap_inner
(pid=4388) self.run()
(pid=4388) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 102, in run
(pid=4388) err_tb = err_tb.format_exc()
(pid=4388) AttributeError: 'traceback' object has no attribute 'format_exc'
(pid=4388)
(pid=4389) 2020-01-30 11:38:58,570 ERROR function_runner.py:96 -- Runner Thread raised error.
(pid=4389) Traceback (most recent call last):
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 90, in run
(pid=4389) self._entrypoint()
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 141, in entrypoint
(pid=4389) return self._trainable_func(config, self._status_reporter)
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 249, in _trainable_func
(pid=4389) output = train_func(config, reporter)
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/search.py", line 90, in ray_trainable
(pid=4389) metrics = Trial(spec).run()
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/control.py", line 181, in run
(pid=4389) metrics = analysis.analyze_trial(self.spec, session_metrics_list)
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 265, in analyze_trial
(pid=4389) trial_metrics = calc_trial_metrics(session_metrics_list, info_prepath)
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 187, in calc_trial_metrics
(pid=4389) frames = session_metrics_list[0]['local']['frames']
(pid=4389) IndexError: list index out of range
(pid=4389) Exception in thread Thread-1:
(pid=4389) Traceback (most recent call last):
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 90, in run
(pid=4389) self._entrypoint()
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 141, in entrypoint
(pid=4389) return self._trainable_func(config, self._status_reporter)
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 249, in _trainable_func
(pid=4389) output = train_func(config, reporter)
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/search.py", line 90, in ray_trainable
(pid=4389) metrics = Trial(spec).run()
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/control.py", line 181, in run
(pid=4389) metrics = analysis.analyze_trial(self.spec, session_metrics_list)
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 265, in analyze_trial
(pid=4389) trial_metrics = calc_trial_metrics(session_metrics_list, info_prepath)
(pid=4389) File "/home/joe/SLM-Lab/slm_lab/experiment/analysis.py", line 187, in calc_trial_metrics
(pid=4389) frames = session_metrics_list[0]['local']['frames']
(pid=4389) IndexError: list index out of range
(pid=4389)
(pid=4389) During handling of the above exception, another exception occurred:
(pid=4389)
(pid=4389) Traceback (most recent call last):
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/threading.py", line 917, in _bootstrap_inner
(pid=4389) self.run()
(pid=4389) File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 102, in run
(pid=4389) err_tb = err_tb.format_exc()
(pid=4389) AttributeError: 'traceback' object has no attribute 'format_exc'
(pid=4389)
2020-01-30 11:38:59,690 ERROR trial_runner.py:497 -- Error processing event.
Traceback (most recent call last):
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 446, in _process_trial
result = self.trial_executor.fetch_result(trial)
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 316, in fetch_result
result = ray.get(trial_future[0])
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/worker.py", line 2197, in get
raise value
ray.exceptions.RayTaskError: ray_worker (pid=4388, host=Gauss)
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/trainable.py", line 151, in train
result = self._train()
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 203, in _train
("Wrapped function ran until completion without reporting "
ray.tune.error.TuneError: Wrapped function ran until completion without reporting results or raising an exception.
2020-01-30 11:38:59,694 INFO ray_trial_executor.py:180 -- Destroying actor for trial ray_trainable_0_agent.0.algorithm.center_return=True,trial_index=0. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
2020-01-30 11:38:59,705 ERROR trial_runner.py:497 -- Error processing event.
Traceback (most recent call last):
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/trial_runner.py", line 446, in _process_trial
result = self.trial_executor.fetch_result(trial)
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/ray_trial_executor.py", line 316, in fetch_result
result = ray.get(trial_future[0])
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/worker.py", line 2197, in get
raise value
ray.exceptions.RayTaskError: ray_worker (pid=4389, host=Gauss)
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/trainable.py", line 151, in train
result = self._train()
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/function_runner.py", line 203, in _train
("Wrapped function ran until completion without reporting "
ray.tune.error.TuneError: Wrapped function ran until completion without reporting results or raising an exception.
2020-01-30 11:38:59,707 INFO ray_trial_executor.py:180 -- Destroying actor for trial ray_trainable_1_agent.0.algorithm.center_return=False,trial_index=1. If your trainable is slow to initialize, consider setting reuse_actors=True to reduce actor creation overheads.
== Status ==
Using FIFO scheduling algorithm.
Resources requested: 0/8 CPUs, 0/1 GPUs
Memory usage on this node: 2.5/16.7 GB
Result logdir: /home/joe/ray_results/reinforce_baseline_cartpole
Number of trials: 2 ({'ERROR': 2})
ERROR trials:
- ray_trainable_0_agent.0.algorithm.center_return=True,trial_index=0: ERROR, 1 failures: /home/joe/ray_results/reinforce_baseline_cartpole/ray_trainable_0_agent.0.algorithm.center_return=True,trial_index=0_2020-01-30_11-38-57n2qc80ke/error_2020-01-30_11-38-59.txt
- ray_trainable_1_agent.0.algorithm.center_return=False,trial_index=1: ERROR, 1 failures: /home/joe/ray_results/reinforce_baseline_cartpole/ray_trainable_1_agent.0.algorithm.center_return=False,trial_index=1_2020-01-30_11-38-57unqmlqvg/error_2020-01-30_11-38-59.txt
Traceback (most recent call last):
File "run_lab.py", line 80, in <module>
main()
File "run_lab.py", line 72, in main
read_spec_and_run(*args)
File "run_lab.py", line 56, in read_spec_and_run
run_spec(spec, lab_mode)
File "run_lab.py", line 35, in run_spec
Experiment(spec).run()
File "/home/joe/SLM-Lab/slm_lab/experiment/control.py", line 203, in run
trial_data_dict = search.run_ray_search(self.spec)
File "/home/joe/SLM-Lab/slm_lab/experiment/search.py", line 124, in run_ray_search
server_port=util.get_port(),
File "/home/joe/anaconda3/envs/lab/lib/python3.7/site-packages/ray/tune/tune.py", line 265, in run
raise TuneError("Trials did not complete", errored_trials)
ray.tune.error.TuneError: ('Trials did not complete', [ray_trainable_0_agent.0.algorithm.center_return=True,trial_index=0, ray_trainable_1_agent.0.algorithm.center_return=False,trial_index=1])
Describe the bug After successfully installing SLM-Lab and proceeding to the "Quick Start" portion which involves running DQN on the CartPole environment, everything works well i.e. (final_return_ma increases).
Command entered: python run_lab.py slm_lab/spec/demo.json dqn_cartpole dev
After several log summary and metric instances an OpenGL error code occurs :
[101017:1015/191313.594764:ERROR:buffer_manager.cc(488)] [.DisplayCompositor]GL ERROR :GL_INVALID_OPERATION : glBufferData: <- error from previous GL command
and then the process seems to end without showing any graphs.
To Reproduce
OS and environment: Ubuntu 20.04 LTS
SLM Lab git SHA (run git rev-parse HEAD to get it):dda02d00031553aeda4c49c5baa7d0706c53996b
spec file used: slm_lab/spec/demo.json
Error logs
![ERROR:buffer_manager.cc(488)] [.DisplayCompositor]GL ERROR :GL_INVALID_OPERATION : glBufferData: <- error from previous GL command](https://avatars.githubusercontent.com/u/51099935?v=4)
[2020-10-15 19:13:09,800 PID:100781 INFO __init__.py log_summary] Trial 0 session 0 dqn_cartpole_t0_s0 [train_df] epi: 123 t: 120 wall_t: 153 opt_step: 398720 frame: 10000 fps: 65.3595 total_reward: 200 total_reward_ma: 142.7 loss: 5.46846 lr: 0.00774841 explore_var: 0.1 entropy_coef: nan entropy: nan grad_norm: 0.230459
[2020-10-15 19:13:09,821 PID:100781 INFO __init__.py log_metrics] Trial 0 session 0 dqn_cartpole_t0_s0 [train_df metrics] final_return_ma: 142.7 strength: 120.84 max_strength: 178.14 final_strength: 178.14 sample_efficiency: 0.00019783 training_efficiency: 5.02079e-06 stability: 0.926742
[100946:1015/191310.923076:ERROR:buffer_manager.cc(488)] [.DisplayCompositor]GL ERROR :GL_INVALID_OPERATION : glBufferData: <- error from previous GL command
[2020-10-15 19:13:12,794 PID:100781 INFO __init__.py log_metrics] Trial 0 session 0 dqn_cartpole_t0_s0 [eval_df metrics] final_return_ma: 142.7 strength: 120.84 max_strength: 178.14 final_strength: 178.14 sample_efficiency: 0.00019783 training_efficiency: 5.02079e-06 stability: 0.926742
[2020-10-15 19:13:12,798 PID:100781 INFO logger.py info] Session 0 done
[101017:1015/191313.594764:ERROR:buffer_manager.cc(488)] [.DisplayCompositor]GL ERROR :GL_INVALID_OPERATION : glBufferData: <- error from previous GL command
[2020-10-15 19:13:15,443 PID:100781 INFO logger.py info] Trial 0 done
Hi, I get stuck at the end of the trial, when it finish, can't create the respective graphics, i got the next traceback error, what can it be?
Traceback (most recent call last):
File "run_lab.py", line 63, in
Hi, i'm having trouble in the installation because the linux distro, can you indicate the packages required for a correct installation for run the "yarn install" command.
It's look a great framework and i'll like to test it, thanks and regards.
Hi, kengz, How to add a non-gym environment, such as Mahjong or Poker enviroment in rlcard project(https://github.com/datamllab/rlcard). Would you provide a simple demo for adding a new non-gym env, or give some suggestions about how to quickly add ?
HI!
I get terminating when i trainning with search mode and connect to env by grpc ,the log like this: "(pid=2023) terminating" and has nothing else logs about this "terminating", my process also killed by it at the same time. why i get that? @kengz @lgraesser
/SLM-Lab/slm_lab/lib/util.py", line 5, in
To Reproduce
Additional context had to add cmake libgcc manually
Error logs
(base) l*@l*-HP-Pavilion-dv7-PC:~/SLM-Lab$ python3 run_lab.py slm_lab/spec/demo.json dqn_cartpole dev
Traceback (most recent call last):
File "run_lab.py", line 10, in
Undefined names have the potential to raise NameError at runtime.
flake8 testing of https://github.com/kengz/SLM-Lab on Python 3.6.3
$ flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics
./slm_lab/agent/algorithm/base.py:73:16: F821 undefined name 'action'
return action
^
./slm_lab/agent/algorithm/base.py:99:16: F821 undefined name 'batch'
return batch
^
./slm_lab/agent/algorithm/policy_util.py:43:13: F821 undefined name 'new_prob'
new_prob[torch.argmax(probs, dim=0)] = 1.0
^
./slm_lab/env/__init__.py:97:49: F821 undefined name 'nvec'
setattr(gym_space, 'low', np.zeros_like(nvec))
^
./slm_lab/experiment/search.py:131:9: F821 undefined name 'config'
config['trial_index'] = self.experiment.info_space.tick('trial')['trial']
^
./slm_lab/experiment/search.py:133:16: F821 undefined name 'config'
return config
^
./slm_lab/experiment/search.py:146:16: F821 undefined name 'trial_data_dict'
return trial_data_dict
^
./test/agent/net/test_nn.py:83:25: F821 undefined name 'net_util'
before_params = net_util.copy_trainable_params(net)
^
./test/agent/net/test_nn.py:88:24: F821 undefined name 'net_util'
after_params = net_util.copy_trainable_params(net)
^
./test/agent/net/test_nn.py:114:25: F821 undefined name 'net_util'
before_params = net_util.copy_fixed_params(net)
^
./test/agent/net/test_nn.py:118:24: F821 undefined name 'net_util'
after_params = net_util.copy_fixed_params(net)
^
11 F821 undefined name 'action'
11
Hi. I tried running this through Docker, and ran into a few gotchas following the gitbook instructions:
bin somehow gave me permission errors, despite being root. pasting these manually helped as a work-around.sudo a lot, but the docker container did not recognize this. removing these helped. fwiw, installing sudo helped as well.source activate lab errored stating source was not recognized. I then tried:# conda config --add channels anaconda
# conda activate lab
# conda env update
(lab) # python3 --version
Python 3.6.4
(lab) # yarn start
$ python3 run_lab.py
Traceback (most recent call last):
File "run_lab.py", line 6, in <module>
from slm_lab.experiment.control import Session, Trial, Experiment
File "/opt/SLM-Lab/slm_lab/__init__.py", line 12
with open(os.path.join(ROOT_DIR, 'config', f'{config_name}.json')) as f:
^
SyntaxError: invalid syntax
error Command failed with exit code 1
Trying this line in this python3 seemed not to yield syntax errors though, so f-strings do seem supported. Weird.
I haven't fully gotten this to work, but hopefully some of this may be useful for the tutorial. I tried looking for the gitbook source in case I could add to the installation instructions based on this, but couldn't find it.
Hello,
I am currently using SLM lab as the learning component of my custom Unity environments. I am using a modified UnityEnv wrapper and I run my experiments using a modified version of the starter code here.
When I am running both PPO and SAC I realized that my Unix kernel kills the job after a while due running out of memory (RAM/Swap).
Given the custom nature of this bug, I don't expect you to replicate it, but rather, asking if you had ever faced a similar problem on your end.
Some more detail:
Any guidance or tips would be appreciated! And once again thank you for the great library!
questionI follow the book "Foundations of Deep Reinforcement Learning" to conduct the experiments of reinformace algorithm.Although the algorithm can be conducted successfully, its graphs fail to be saved successfully, with an error from orca "service unavaialble".
spec file used: reinforce_cartpole.jsonAdditional context Add any other context about the problem here.
Error logs Failed to generate graph. Run retro-analysis to generate graphs later. The image request was rejected by the orca conversion utility with the following error: 503:
The server is temporarily unable to service your request due to maintenance downtime or capacity problems. Please try again later.
Exception: pyglet 2.0.0 requires Python 3.8 or newer. After launch this command for the demo "python run_lab.py slm_lab/spec/demo.json dqn_cartpole dev" i get this error "Exception: pyglet 2.0.0 requires Python 3.8 or newer." I.ve checked tha the python version in the lab conda environment is 3.7.3
To Reproduce
Error logs
Traceback (most recent call last):
File "run_lab.py", line 99, in
Hi, I am interested in creating my own environment on gym open ai and train and evaluate different slm-lab algorithms on it. Can you kindly guide me how can i add the custom created gym environment in the spec files. I am new to it so I will highly appreciate it you can explain it in laymen terms.
Describe the bug running docker build hits an error during the build process
To Reproduce
git rev-parse HEAD to get it): 2890277c8d499dbc925a16bda40acd8c29cb6819spec file used: unknownAdditional context this appears to be caused by a problem earlier in the Dockerfile, where the python-pyglet package is failing to install.
Error logs
> [7/9] RUN . ~/miniconda3/etc/profile.d/conda.sh && conda create -n lab python=3.7.3 -y && conda activate lab && conda env update -f environment.yml && conda clean -y --all && rm -rf ~/.cache/pip:
#11 1.493 Collecting package metadata (current_repodata.json): ...working... done
#11 5.422 Solving environment: ...working... failed with repodata from current_repodata.json, will retry with next repodata source.
#11 5.424 Collecting package metadata (repodata.json): ...working... done
#11 15.34 Solving environment: ...working... done
#11 15.85
#11 15.85
#11 15.85 ==> WARNING: A newer version of conda exists. <==
#11 15.85 current version: 4.12.0
#11 15.85 latest version: 4.14.0
#11 15.85
#11 15.85 Please update conda by running
#11 15.85
#11 15.85 $ conda update -n base -c defaults conda
#11 15.85
#11 15.85
#11 15.93
#11 15.93 ## Package Plan ##
#11 15.93
#11 15.93 environment location: /root/miniconda3/envs/lab
#11 15.93
#11 15.93 added / updated specs:
#11 15.93 - python=3.7.3
#11 15.93
#11 15.93
#11 15.93 The following packages will be downloaded:
#11 15.93
#11 15.93 package | build
#11 15.93 ---------------------------|-----------------
#11 15.93 _openmp_mutex-5.1 | 1_gnu 21 KB
#11 15.93 ca-certificates-2022.07.19 | h06a4308_0 124 KB
#11 15.93 certifi-2022.6.15 | py37h06a4308_0 153 KB
#11 15.93 libedit-3.1.20210910 | h7f8727e_0 166 KB
#11 15.93 libffi-3.2.1 | hf484d3e_1007 48 KB
#11 15.93 libgcc-ng-11.2.0 | h1234567_1 5.3 MB
#11 15.93 libgomp-11.2.0 | h1234567_1 474 KB
#11 15.93 libstdcxx-ng-11.2.0 | h1234567_1 4.7 MB
#11 15.93 ncurses-6.3 | h5eee18b_3 781 KB
#11 15.93 openssl-1.1.1q | h7f8727e_0 2.5 MB
#11 15.93 pip-22.1.2 | py37h06a4308_0 2.4 MB
#11 15.93 python-3.7.3 | h0371630_0 32.1 MB
#11 15.93 readline-7.0 | h7b6447c_5 324 KB
#11 15.93 setuptools-63.4.1 | py37h06a4308_0 1.1 MB
#11 15.93 sqlite-3.33.0 | h62c20be_0 1.1 MB
#11 15.93 tk-8.6.12 | h1ccaba5_0 3.0 MB
#11 15.93 xz-5.2.5 | h7f8727e_1 339 KB
#11 15.93 zlib-1.2.12 | h7f8727e_2 106 KB
#11 15.93 ------------------------------------------------------------
#11 15.93 Total: 54.8 MB
#11 15.93
#11 15.93 The following NEW packages will be INSTALLED:
#11 15.93
#11 15.93 _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main
#11 15.93 _openmp_mutex pkgs/main/linux-64::_openmp_mutex-5.1-1_gnu
#11 15.93 ca-certificates pkgs/main/linux-64::ca-certificates-2022.07.19-h06a4308_0
#11 15.93 certifi pkgs/main/linux-64::certifi-2022.6.15-py37h06a4308_0
#11 15.93 libedit pkgs/main/linux-64::libedit-3.1.20210910-h7f8727e_0
#11 15.93 libffi pkgs/main/linux-64::libffi-3.2.1-hf484d3e_1007
#11 15.93 libgcc-ng pkgs/main/linux-64::libgcc-ng-11.2.0-h1234567_1
#11 15.93 libgomp pkgs/main/linux-64::libgomp-11.2.0-h1234567_1
#11 15.93 libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-11.2.0-h1234567_1
#11 15.93 ncurses pkgs/main/linux-64::ncurses-6.3-h5eee18b_3
#11 15.93 openssl pkgs/main/linux-64::openssl-1.1.1q-h7f8727e_0
#11 15.93 pip pkgs/main/linux-64::pip-22.1.2-py37h06a4308_0
#11 15.93 python pkgs/main/linux-64::python-3.7.3-h0371630_0
#11 15.93 readline pkgs/main/linux-64::readline-7.0-h7b6447c_5
#11 15.93 setuptools pkgs/main/linux-64::setuptools-63.4.1-py37h06a4308_0
#11 15.93 sqlite pkgs/main/linux-64::sqlite-3.33.0-h62c20be_0
#11 15.93 tk pkgs/main/linux-64::tk-8.6.12-h1ccaba5_0
#11 15.93 wheel pkgs/main/noarch::wheel-0.37.1-pyhd3eb1b0_0
#11 15.93 xz pkgs/main/linux-64::xz-5.2.5-h7f8727e_1
#11 15.93 zlib pkgs/main/linux-64::zlib-1.2.12-h7f8727e_2
#11 15.93
#11 15.93
#11 15.93
#11 15.93 Downloading and Extracting Packages
zlib-1.2.12 | 106 KB | ########## | 100%
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#11 24.41 Preparing transaction: ...working... done
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#11 25.93 Executing transaction: ...working... done
#11 28.11 #
#11 28.11 # To activate this environment, use
#11 28.11 #
#11 28.11 # $ conda activate lab
#11 28.11 #
#11 28.11 # To deactivate an active environment, use
#11 28.11 #
#11 28.11 # $ conda deactivate
#11 28.11
#11 29.82 Collecting package metadata (repodata.json): ...working... done
#11 101.5 Solving environment: ...working... done
#11 148.2
#11 148.2
#11 148.2 ==> WARNING: A newer version of conda exists. <==
#11 148.2 current version: 4.12.0
#11 148.2 latest version: 4.14.0
#11 148.2
#11 148.2 Please update conda by running
#11 148.2
#11 148.2 $ conda update -n base -c defaults conda
#11 148.2
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#11 148.3 Downloading and Extracting Packages
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#11 419.8 Preparing transaction: ...working... done
#11 422.4 Verifying transaction: ...working... done
#11 425.0 Executing transaction: ...working... By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html
#11 436.6
#11 436.6 done
#11 437.1 Installing pip dependencies: ...working... Ran pip subprocess with arguments:
#11 818.8 ['/root/miniconda3/envs/lab/bin/python', '-m', 'pip', 'install', '-U', '-r', '/root/SLM-Lab/condaenv.u9_zu190.requirements.txt']
#11 818.8 Pip subprocess output:
#11 818.8 Collecting box2d-py==2.3.8 (from -r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 1))
#11 818.8 Downloading https://files.pythonhosted.org/packages/87/34/da5393985c3ff9a76351df6127c275dcb5749ae0abbe8d5210f06d97405d/box2d_py-2.3.8-cp37-cp37m-manylinux1_x86_64.whl (448kB)
#11 818.8 Collecting cloudpickle==0.5.2 (from -r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 2))
#11 818.8 Downloading https://files.pythonhosted.org/packages/aa/18/514b557c4d8d4ada1f0454ad06c845454ad438fd5c5e0039ba51d6b032fe/cloudpickle-0.5.2-py2.py3-none-any.whl
#11 818.8 Collecting colorlover==0.3.0 (from -r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 3))
#11 818.8 Downloading https://files.pythonhosted.org/packages/9a/53/f696e4480b1d1de3b1523991dea71cf417c8b19fe70c704da164f3f90972/colorlover-0.3.0-py3-none-any.whl
#11 818.8 Collecting future==0.18.2 (from -r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 4))
#11 818.8 Downloading https://files.pythonhosted.org/packages/45/0b/38b06fd9b92dc2b68d58b75f900e97884c45bedd2ff83203d933cf5851c9/future-0.18.2.tar.gz (829kB)
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#11 818.8 Requirement already satisfied, skipping upgrade: zipp>=0.5 in /root/miniconda3/envs/lab/lib/python3.7/site-packages (from importlib-metadata; python_version < "3.8"->click->ray==0.7.0->-r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 8)) (3.8.1)
#11 818.8 Requirement already satisfied, skipping upgrade: typing-extensions>=3.6.4; python_version < "3.8" in /root/miniconda3/envs/lab/lib/python3.7/site-packages (from importlib-metadata; python_version < "3.8"->click->ray==0.7.0->-r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 8)) (4.3.0)
#11 818.8 Collecting pyasn1>=0.1.3 (from rsa<5,>=3.1.4; python_version >= "3.6"->google-auth<2,>=1.6.3->tensorboard==2.1.1->-r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 10))
#11 818.8 Downloading https://files.pythonhosted.org/packages/62/1e/a94a8d635fa3ce4cfc7f506003548d0a2447ae76fd5ca53932970fe3053f/pyasn1-0.4.8-py2.py3-none-any.whl (77kB)
#11 818.8 Collecting oauthlib>=3.0.0 (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.1.1->-r /root/SLM-Lab/condaenv.u9_zu190.requirements.txt (line 10))
#11 818.8 Downloading https://files.pythonhosted.org/packages/1d/46/5ee2475e1b46a26ca0fa10d3c1d479577fde6ee289f8c6aa6d7ec33e31fd/oauthlib-3.2.0-py3-none-any.whl (151kB)
#11 818.8 Building wheels for collected packages: future, pyopengl, xvfbwrapper, gym, typing, grpcio, MarkupSafe
#11 818.8 Building wheel for future (setup.py): started
#11 818.8 Building wheel for future (setup.py): finished with status 'done'
#11 818.8 Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e
#11 818.8 Building wheel for pyopengl (setup.py): started
#11 818.8 Building wheel for pyopengl (setup.py): finished with status 'done'
#11 818.8 Stored in directory: /root/.cache/pip/wheels/6c/00/7f/1dd736f380848720ad79a1a1de5272e0d3f79c15a42968fb58
#11 818.8 Building wheel for xvfbwrapper (setup.py): started
#11 818.8 Building wheel for xvfbwrapper (setup.py): finished with status 'done'
#11 818.8 Stored in directory: /root/.cache/pip/wheels/10/f2/61/cacfaf84b352c223761ea8d19616e3b5ac5c27364da72863f0
#11 818.8 Building wheel for gym (setup.py): started
#11 818.8 Building wheel for gym (setup.py): finished with status 'done'
#11 818.8 Stored in directory: /root/.cache/pip/wheels/57/b0/13/4153e1acab826fbe612c95b1336a63a3fa6416902a8d74a1b7
#11 818.8 Building wheel for typing (setup.py): started
#11 818.8 Building wheel for typing (setup.py): finished with status 'done'
#11 818.8 Stored in directory: /root/.cache/pip/wheels/2d/04/41/8e1836e79581989c22eebac3f4e70aaac9af07b0908da173be
#11 818.8 Building wheel for grpcio (setup.py): started
#11 818.8 Building wheel for grpcio (setup.py): still running...
#11 818.8 Building wheel for grpcio (setup.py): still running...
#11 818.8 Building wheel for grpcio (setup.py): finished with status 'error'
#11 818.8 Running setup.py clean for grpcio
#11 818.8 Building wheel for MarkupSafe (setup.py): started
#11 818.8 Building wheel for MarkupSafe (setup.py): finished with status 'done'
#11 818.8 Stored in directory: /root/.cache/pip/wheels/f5/40/34/d60ef965622011684037ea53e53fd44ef58ed2062f26878ce2
#11 818.8 Successfully built future pyopengl xvfbwrapper gym typing MarkupSafe
#11 818.8 Failed to build grpcio
#11 818.8 Installing collected packages: box2d-py, cloudpickle, colorlover, future, kaleido, opencv-python, pyopengl, typing, funcsigs, click, colorama, flatbuffers, redis, filelock, ray, absl-py, pyasn1, rsa, pyasn1-modules, cachetools, google-auth, markdown, MarkupSafe, werkzeug, charset-normalizer, idna, urllib3, requests, oauthlib, requests-oauthlib, google-auth-oauthlib, grpcio, protobuf, tensorboard, xvfbwrapper, pyglet, gym, pybullet, roboschool, atari-py
#11 818.8 Running setup.py install for grpcio: started
#11 818.8 Running setup.py install for grpcio: still running...
#11 818.8 Running setup.py install for grpcio: still running...
#11 818.8 Running setup.py install for grpcio: finished with status 'error'
#11 818.8 Pip subprocess error:
#11 818.8 ERROR: Complete output from command /root/miniconda3/envs/lab/bin/python -u -c 'import setuptools, tokenize;__file__='"'"'/tmp/pip-install-n1qdzi5c/grpcio/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' bdist_wheel -d /tmp/pip-wheel-gt9n7xut --python-tag cp37:
#11 818.8 ERROR: Found cython-generated files...
#11 818.8 running bdist_wheel
#11 818.8 running build
#11 818.8 running build_py
#11 818.8 running build_project_metadata
#11 818.8 creating python_build
#11 818.8 creating python_build/lib.linux-x86_64-cpython-37
#11 818.8 creating python_build/lib.linux-x86_64-cpython-37/grpc
#11 818.8 copying src/python/grpcio/grpc/_channel.py -> python_build/lib.linux-x86_64-cpython-37/grpc
#11 818.8 copying src/python/grpcio/grpc/_utilities.py -> python_build/lib.linux-x86_64-cpython-37/grpc
...
...
^
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/boringssl-with-bazel/src/crypto/base64/base64.c -o python_build/temp.linux-x86_64-cpython-37/third_party/boringssl-with-bazel/src/crypto/base64/base64.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1: warning: command line option ‘-std=c++14’ is valid for C++/ObjC++ but not for C
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/abseil-cpp/absl/strings/internal/str_format/bind.cc -o python_build/temp.linux-x86_64-cpython-37/third_party/abseil-cpp/absl/strings/internal/str_format/bind.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/abseil-cpp/absl/time/internal/cctz/src/time_zone_lookup.cc -o python_build/temp.linux-x86_64-cpython-37/third_party/abseil-cpp/absl/time/internal/cctz/src/time_zone_lookup.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/boringssl-with-bazel/src/crypto/cpu-aarch64-fuchsia.c -o python_build/temp.linux-x86_64-cpython-37/third_party/boringssl-with-bazel/src/crypto/cpu-aarch64-fuchsia.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1: warning: command line option ‘-std=c++14’ is valid for C++/ObjC++ but not for C
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/boringssl-with-bazel/src/crypto/cpu-aarch64-linux.c -o python_build/temp.linux-x86_64-cpython-37/third_party/boringssl-with-bazel/src/crypto/cpu-aarch64-linux.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1: warning: command line option ‘-std=c++14’ is valid for C++/ObjC++ but not for C
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/boringssl-with-bazel/src/crypto/cpu-aarch64-win.c -o python_build/temp.linux-x86_64-cpython-37/third_party/boringssl-with-bazel/src/crypto/cpu-aarch64-win.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1: warning: command line option ‘-std=c++14’ is valid for C++/ObjC++ but not for C
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/boringssl-with-bazel/src/crypto/cpu-arm-linux.c -o python_build/temp.linux-x86_64-cpython-37/third_party/boringssl-with-bazel/src/crypto/cpu-arm-linux.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1: warning: command line option ‘-std=c++14’ is valid for C++/ObjC++ but not for C
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/boringssl-with-bazel/src/crypto/cpu-arm.c -o python_build/temp.linux-x86_64-cpython-37/third_party/boringssl-with-bazel/src/crypto/cpu-arm.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 cc1: warning: command line option ‘-std=c++14’ is valid for C++/ObjC++ but not for C
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m -c third_party/boringssl-with-bazel/src/crypto/cpu-intel.c -o python_build/temp.linux-x86_64-cpython-37/third_party/boringssl-with-bazel/src/crypto/cpu-intel.o -std=c++14 -fvisibility=hidden -fno-wrapv -fno-exceptions -pthread
#11 818.8 gcc -pthread -B /root/miniconda3/envs/lab/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -D_WIN32_WINNT=1536 -DGRPC_XDS_USER_AGENT_NAME_SUFFIX=\"Python\" -DGRPC_XDS_USER_AGENT_VERSION_SUFFIX=\"1.48.0\" -DGPR_BACKWARDS_COMPATIBILITY_MODE=1 -DHAVE_CONFIG_H=1 -DGRPC_ENABLE_FORK_SUPPORT=1 "-DPyMODINIT_FUNC=extern \"C\" __attribute__((visibility (\"default\"))) PyObject*" -DGRPC_POSIX_FORK_ALLOW_PTHREAD_ATFORK=1 -Isrc/python/grpcio -Iinclude -I. -Ithird_party/abseil-cpp -Ithird_party/address_sorting/include -Ithird_party/cares/cares/include -Ithird_party/cares -Ithird_party/cares/cares -Ithird_party/cares/config_linux -Ithird_party/re2 -Ithird_party/boringssl-with-bazel/src/include -Ithird_party/upb -Isrc/core/ext/upb-generated -Isrc/core/ext/upbdefs-generated -Ithird_party/xxhash -Ithird_party/zlib -I/root/miniconda3/envs/lab/include/python3.7m
#11 818.8 [output clipped, log limit 1MiB reached]
#11 818.8
#11 818.8 failed
------
executor failed running [/bin/bash -c . ~/miniconda3/etc/profile.d/conda.sh && conda create -n lab python=3.7.3 -y && conda activate lab && conda env update -f environment.yml && conda clean -y --all && rm -rf ~/.cache/pip]: exit code: 1
I really appreciate to you for your book, It's a great help for me to start RL. ^^
Describe the bug A clear and concise description of what the bug is. When executing example code 4.7 (vanilla_dpn without any change), there comes a warning msg as below
To Reproduce
git rev-parse HEAD to get it): 5fa5ee3d034a38d5644f6f96b4c02ec366c831d0 (from the file "SLM-lab/data/vanilla_dqn_boltzmann_cartpole_2022_07_15_092012/vanilla_dqn_boltzmann_cartpole_t0_spec.json")spec file used: SLM-lab/slm_lab/spec/benchmark/dqn/dqn_cartpole.jsonAdditional context After it occurred, it proceeded too slow (it took over an hour) than other methods (15 minutes for SARSA), and the result is also strange that mean_returns_ma decreases gradually to about 50 after 30k frames. I wonder the result of this trial is related to the warning situation
Error logs
[2022-07-15 09:20:14,002 PID:245693 INFO logger.py info] Running RL loop for trial 0 session 3
[2022-07-15 09:20:14,006 PID:245693 INFO __init__.py log_summary] Trial 0 session 3 vanilla_dqn_boltzmann_cartpole_t0_s3 [train_df] epi: 0 t: 0 wall_t: 0 opt_step: 0 frame: 0 fps: 0 total_reward: nan total_reward_ma: nan loss: nan lr: 0.01 explore_var: 5 entropy_coef: nan entropy: nan grad_norm: nan
/home/eric/miniconda3/envs/lab/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:100: UserWarning:
Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
/home/eric/miniconda3/envs/lab/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:100: UserWarning:
Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
/home/eric/miniconda3/envs/lab/lib/python3.7/site-packages/torch/optim/lr_scheduler.py:100: UserWarning:
Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
Hi kengz, I find that the convergence performance of training loss (=value loss+policy loss) of ppo algorithem applied in game pong is poor (see Fig.1), but the corresponding mean_returns shows a good upward trend and reaches convergence (see Fig.2).
That is why? how to improve the convergence performance of training loss? I tried many imporved tricks with ppo, but none of them worked.
Fig.1
Fig.2
Full Changelog: https://github.com/kengz/SLM-Lab/compare/v4.2.3...v4.2.4
Source code(tar.gz)reinforce_pong.json spec to prevent confusion in https://github.com/kengz/SLM-Lab/pull/499Full Changelog: https://github.com/kengz/SLM-Lab/compare/v4.2.2...v4.2.3
Source code(tar.gz):raised_hands: Thanks to @Nickfagiano help with debugging.
atari-py version to 0.2.6 for safety:raised_hands: Thanks to @piosif97 for helping.
:raised_hands: Thanks to @vladimirnitu and @steindaian for providing the PDF.
Source code(tar.gz)Dependencies and systems around SLM Lab has changed and caused some breakages. This release fixes these installation issues.
homebrew/cask (thanks @ben-e, @amjadmajid )train@ resume mode and refactors the enjoy mode. See PR for detailed info.train@ usage exampleSpecify train mode as train@{predir}, where {predir} is the data directory of the last training run, or simply uselatest` to use the latest. e.g.:
python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole train
# terminate run before its completion
# optionally edit the spec file in a past-future-consistent manner
# run resume with either of the commands:
python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole [email protected]
# or to use a specific run folder
python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole [email protected]/reinforce_cartpole_2020_04_13_232521
enjoy mode refactorThe train@ resume mode API allows for the enjoy mode to be refactored. Both share similar syntax. Continuing with the example above, to enjoy a train model, we now use:
python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole [email protected]/reinforce_cartpole_2020_04_13_232521/reinforce_cartpole_t0_s0_spec.json
OnPolicyCrossEntropy memory class. See PR for details. Credits to @ingambe.||||||||
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Env. \ Alg. | DQN | DDQN+PER | A2C (GAE) | A2C (n-step) | PPO | SAC |
| Breakout | 80.88 | 182 | 377 | 398 | 443 | 3.51* |
| Pong graph
| 18.48 | 20.5 | 19.31 | 19.56 | 20.58 | 19.87* |
| Seaquest graph
| 1185 | 4405 | 1070 | 1684 | 1715 | 171* |
| Qbert graph
| 5494 | 11426 | 12405 | 13590 | 13460 | 923* |
| LunarLander graph
| 192 | 233 | 25.21 | 68.23 | 214 | 276 |
| UnityHallway graph
| -0.32 | 0.27 | 0.08 | -0.96 | 0.73 | 0.01 |
| UnityPushBlock graph
| 4.88 | 4.93 | 4.68 | 4.93 | 4.97 | -0.70 |graph
Episode score at the end of training attained by SLM Lab implementations on discrete-action control problems. Reported episode scores are the average over the last 100 checkpoints, and then averaged over 4 Sessions. A Random baseline with score averaged over 100 episodes is included. Results marked with
*were trained using the hybrid synchronous/asynchronous version of SAC to parallelize and speed up training time. For SAC, Breakout, Pong and Seaquest were trained for 2M frames instead of 10M frames.
Source code(tar.gz)For the full Atari benchmark, see Atari Benchmark
This marks a stable release of SLM Lab with full benchmark results
tensorboard --logdir=data after a session/trial is completed. Example screenshot:
||||||||
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Env. \ Alg. | DQN | DDQN+PER | A2C (GAE) | A2C (n-step) | PPO | SAC |
| Breakout | 80.88 | 182 | 377 | 398 | 443 | - |
| Pong graph
| 18.48 | 20.5 | 19.31 | 19.56 | 20.58 | 19.87* |
| Seaquest graph
| 1185 | 4405 | 1070 | 1684 | 1715 | - |
| Qbert graph
| 5494 | 11426 | 12405 | 13590 | 13460 | 214* |
| LunarLander graph
| 192 | 233 | 25.21 | 68.23 | 214 | 276 |
| UnityHallway graph
| -0.32 | 0.27 | 0.08 | -0.96 | 0.73 | - |
| UnityPushBlock graph
| 4.88 | 4.93 | 4.68 | 4.93 | 4.97 | - |graph
Episode score at the end of training attained by SLM Lab implementations on discrete-action control problems. Reported episode scores are the average over the last 100 checkpoints, and then averaged over 4 Sessions. Results marked with
*were trained using the hybrid synchronous/asynchronous version of SAC to parallelize and speed up training time.
For the full Atari benchmark, see Atari Benchmark
||||||
|:---:|:---:|:---:|:---:|:---:|
| Env. \ Alg. | A2C (GAE) | A2C (n-step) | PPO | SAC |
| RoboschoolAnt | 787 | 1396 | 1843 | 2915 |
| RoboschoolAtlasForwardWalk graph
| 59.87 | 88.04 | 172 | 800 |
| RoboschoolHalfCheetah graph
| 712 | 439 | 1960 | 2497 |
| RoboschoolHopper graph
| 710 | 285 | 2042 | 2045 |
| RoboschoolInvertedDoublePendulum graph
| 996 | 4410 | 8076 | 8085 |
| RoboschoolInvertedPendulum graph
| 995 | 978 | 986 | 941 |
| RoboschoolReacher graph
| 12.9 | 10.16 | 19.51 | 19.99 |
| RoboschoolWalker2d graph
| 280 | 220 | 1660 | 1894 |
| RoboschoolHumanoid graph
| 99.31 | 54.58 | 2388 | 2621* |
| RoboschoolHumanoidFlagrun graph
| 73.57 | 178 | 2014 | 2056* |
| RoboschoolHumanoidFlagrunHarder graph
| -429 | 253 | 680 | 280* |
| Unity3DBall graph
| 33.48 | 53.46 | 78.24 | 98.44 |
| Unity3DBallHard graph
| 62.92 | 71.92 | 91.41 | 97.06 |graph
Episode score at the end of training attained by SLM Lab implementations on continuous control problems. Reported episode scores are the average over the last 100 checkpoints, and then averaged over 4 Sessions. Results marked with
*require 50M-100M frames, so we use the hybrid synchronous/asynchronous version of SAC to parallelize and speed up training time.
|||||||
|:---:|:---:|:---:|:---:|:---:|:---:|
| Env. \ Alg. | DQN | DDQN+PER | A2C (GAE) | A2C (n-step) | PPO |
| Adventure | -0.94 | -0.92 | -0.77 | -0.85 | -0.3 |
| AirRaid graph
| 1876 | 3974 | 4202 | 3557 | 4028 |
| Alien graph
| 822 | 1574 | 1519 | 1627 | 1413 |
| Amidar graph
| 90.95 | 431 | 577 | 418 | 795 |
| Assault graph
| 1392 | 2567 | 3366 | 3312 | 3619 |
| Asterix graph
| 1253 | 6866 | 5559 | 5223 | 6132 |
| Asteroids graph
| 439 | 426 | 2951 | 2147 | 2186 |
| Atlantis graph
| 68679 | 644810 | 2747371 | 2259733 | 2148077 |
| BankHeist graph
| 131 | 623 | 855 | 1170 | 1183 |
| BattleZone graph
| 6564 | 6395 | 4336 | 4533 | 13649 |
| BeamRider graph
| 2799 | 5870 | 2659 | 4139 | 4299 |
| Berzerk graph
| 319 | 401 | 1073 | 763 | 860 |
| Bowling graph
| 30.29 | 39.5 | 24.51 | 23.75 | 31.64 |
| Boxing graph
| 72.11 | 90.98 | 1.57 | 1.26 | 96.53 |
| Breakout graph
| 80.88 | 182 | 377 | 398 | 443 |
| Carnival graph
| 4280 | 4773 | 2473 | 1827 | 4566 |
| Centipede graph
| 1899 | 2153 | 3909 | 4202 | 5003 |
| ChopperCommand graph
| 1083 | 4020 | 3043 | 1280 | 3357 |
| CrazyClimber graph
| 46984 | 88814 | 106256 | 109998 | 116820 |
| Defender graph
| 281999 | 313018 | 665609 | 657823 | 534639 |
| DemonAttack graph
| 1705 | 19856 | 23779 | 19615 | 121172 |
| DoubleDunk graph
| -21.44 | -22.38 | -5.15 | -13.3 | -6.01 |
| ElevatorAction graph
| 32.62 | 17.91 | 9966 | 8818 | 6471 |
| Enduro graph
| 437 | 959 | 787 | 0.0 | 1926 |
| FishingDerby graph
| -88.14 | -1.7 | 16.54 | 1.65 | 36.03 |
| Freeway graph
| 24.46 | 30.49 | 30.97 | 0.0 | 32.11 |
| Frostbite graph
| 98.8 | 2497 | 277 | 261 | 1062 |
| Gopher graph
| 1095 | 7562 | 929 | 1545 | 2933 |
| Gravitar graph
| 87.34 | 258 | 313 | 433 | 223 |
| Hero graph
| 1051 | 12579 | 16502 | 19322 | 17412 |
| IceHockey graph
| -14.96 | -14.24 | -5.79 | -6.06 | -6.43 |
| Jamesbond graph
| 44.87 | 702 | 521 | 453 | 561 |
| JourneyEscape graph
| -4818 | -2003 | -921 | -2032 | -1094 |
| Kangaroo graph
| 1965 | 8897 | 67.62 | 554 | 4989 |
| Krull graph
| 5522 | 6650 | 7785 | 6642 | 8477 |
| KungFuMaster graph
| 2288 | 16547 | 31199 | 25554 | 34523 |
| MontezumaRevenge graph
| 0.0 | 0.02 | 0.08 | 0.19 | 1.08 |
| MsPacman graph
| 1175 | 2215 | 1965 | 2158 | 2350 |
| NameThisGame graph
| 3915 | 4474 | 5178 | 5795 | 6386 |
| Phoenix graph
| 2909 | 8179 | 16345 | 13586 | 30504 |
| Pitfall graph
| -68.83 | -73.65 | -101 | -31.13 | -35.93 |
| Pong graph
| 18.48 | 20.5 | 19.31 | 19.56 | 20.58 |
| Pooyan graph
| 1958 | 2741 | 2862 | 2531 | 6799 |
| PrivateEye graph
| 784 | 303 | 93.22 | 78.07 | 50.12 |
| Qbert graph
| 5494 | 11426 | 12405 | 13590 | 13460 |
| Riverraid graph
| 953 | 10492 | 8308 | 7565 | 9636 |
| RoadRunner graph
| 15237 | 29047 | 30152 | 31030 | 32956 |
| Robotank graph
| 3.43 | 9.05 | 2.98 | 2.27 | 2.27 |
| Seaquest graph
| 1185 | 4405 | 1070 | 1684 | 1715 |
| Skiing graph
| -14094 | -12883 | -19481 | -14234 | -24713 |
| Solaris graph
| 612 | 1396 | 2115 | 2236 | 1892 |
| SpaceInvaders graph
| 451 | 670 | 733 | 750 | 797 |
| StarGunner graph
| 3565 | 38238 | 44816 | 48410 | 60579 |
| Tennis graph
| -23.78 | -10.33 | -22.42 | -19.06 | -11.52 |
| TimePilot graph
| 2819 | 1884 | 3331 | 3440 | 4398 |
| Tutankham graph
| 35.03 | 159 | 161 | 175 | 211 |
| UpNDown graph
| 2043 | 11632 | 89769 | 18878 | 262208 |
| Venture graph
| 4.56 | 9.61 | 0.0 | 0.0 | 11.84 |
| VideoPinball graph
| 8056 | 79730 | 35371 | 40423 | 58096 |
| WizardOfWor graph
| 869 | 328 | 1516 | 1247 | 4283 |
| YarsRevenge graph
| 5816 | 15698 | 27097 | 11742 | 10114 |
| Zaxxon graph
| 442 | 54.28 | 64.72 | 24.7 | 641 |graph
Source code(tar.gz)The table above presents results for 62 Atari games. All agents were trained for 10M frames (40M including skipped frames). Reported results are the episode score at the end of training, averaged over the previous 100 evaluation checkpoints with each checkpoint averaged over 4 Sessions. Agents were checkpointed every 10k training frames.
This release adds a new algorithm: Soft Actor-Critic (SAC).
-implement the original paper: "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor" https://arxiv.org/abs/1801.01290 #398
GumbelSoftmax distribution (custom)Note that the Roboschool reward scales are different from MuJoCo's.
| Env. \ Alg. | SAC |
|:---|---|
| RoboschoolAnt | 2451.55 |
| RoboschoolHalfCheetah | 2004.27 graph
|
| RoboschoolHopper | 2090.52 graph
|
| RoboschoolWalker2d | 1711.92 graph
|graph
| | |
|---|---|
|
|
|
| Trial graph | Moving average |
This release corrects and optimizes all the algorithms from benchmarking on Atari. New metrics are introduced. The lab's API is also redesigned for simplicity.
This benchmark table is pulled from PR396. See the full benchmark results here.
| Env. \ Alg. | A2C (GAE) | A2C (n-step) | PPO | DQN | DDQN+PER |
|:---|---|---|---|---|---|
| Breakout | 389.99 graph
| 391.32 graph
| 425.89 graph
| 65.04 graph
| 181.72 graph
|
| Pong graph
| 20.04 graph
| 19.66 graph
| 20.09 graph
| 18.34 graph
| 20.44 graph
|
| Qbert graph
| 13,328.32 graph
| 13,259.19 graph
| 13,691.89 graph
| 4,787.79 graph
| 11,673.52 graph
|
| Seaquest graph
| 892.68 graph
| 1,686.08 graph
| 1,583.04 graph
| 1,118.50 graph
| 3,751.34 graph
|graph
Now, the full list of algorithms are:
rigorous_eval #390 , and using inference for fast eval #391#275 #278 #279 #280
This release adds an eval mode that is the same as OpenAI baseline. Spawn 2 environments, 1 for training and 1 more eval. In the same process (blocking), run training as usual, then at ckpt, run an episode on eval env and update stats.
The logic for the stats are the same as before, except the original body.df is now split into two: body.train_df and body.eval_df. Eval df uses the main env stats except for t, reward to reflect progress on eval env. Correspondingly, session analysis also produces both versions of data.
Data from body.eval_df is used to generate session_df, session_graph, session_fitness_df, whereas the data from body.train_df is used to generate a new set of trainsession_df, trainsession_graph, trainsession_fitness_df for debugging.
The previous process-based eval functionality is kept, but is now considered as parallel_eval. This can be useful for more robust checkpointing and eval.
#279
This also speeds up run time by x2. For Atari Beamrider with DQN on V100 GPU, manual benchmark measurement gives 110 FPS for training every 4 frames, while eval achieves 160 FPS. This translates to 10M frames in roughly 24 hours.
Source code(tar.gz)yarn retro_eval data/reinforce_cartpole_2018_01_22_211751eval_session_df causing trial analysis to break; add reset_index for safetyuse_gae and use_nstep param to infer from lam, num_step_returnsstart_step offset, add unit tests for rate decay methodsmax_total_t, max_epi to max_tick and max_tick_unit for directness. retire graph_x for the unit above#252 #257 #261 #267 Evaluation sessions during training on a subprocess. This does not interfere with the training process, but spawns multiple subprocesses to do independent evaluation, which then adds to an eval file, and at the end a final eval will finish and plot all the graphs and save all the data for eval.
'training_eval'NUM_EVAL_EPI in analysis.pyenjoy and eval mode syntax. see README.ckpt-epi10-totalt1000eval mode to lab. runs on a checkpoint file. see belowpython run_lab.py data/dqn_cartpole_2018_12_20_214412/dqn_cartpole_t0_spec.json dqn_cartpole [email protected]_cartpole_t0_s2_ckpt-epi10-totalt1000eval_session_df.csveval_trial_graph.png, and an accompanying trial_df as average of all session_dfsepi and total_t. This allows one to eval using the ckpt modelspec.meta.training_eval intrainmode, a subprocess will launch using the ckpt prepath to run an eval Session, using the same way abovepython run_lab.py data/dqn_cartpole_2018_12_20_214412/dqn_cartpole_t0_spec.json dqn_cartpole [email protected]_cartpole_t0_s2_ckpt-epi10-totalt1000`Example eval trial graph:
PRs included #240 #241 #239 #238 #244 #248
To accommodate more advanced features and improvements, all the networks have been improved with better spec and code design, faster operations, and added features
optim.lr_scheduler for learning rate decay. retire old methods.clip_grad, lr_scheduler_specOnpolicyConcatReplaypreprocess_state logic in onpolicy memoriesVarScheduler similar to pytorch's LR scheduler. use clock with flexible scheduling units epi or total_tclock.max_tick_unit specified from envaction_policy_update, update agent spec to explore_var_specentropy_coef with entropy_coef_specclip_eps with clip_eps_spec (PPO)math_util.pymath_util.py from algorithm/ to lib/max_episode to max_epimax_timestep to max_tsave_epi_frequency to save_frequencytraininig_min_timestep to training_start_stepmax_epi as well as max_total_t. propagate clock unit usagemax_tick, max_tick_unit properties to env and clock from abovesave_frequency to use the same units accordinglymax_total_t as end-condition0.3.1 to 0.5.3 to address broken GPU with pytorch 1.0.0#243 #245
ckpt for loading ckpt model. Example usage: yarn start pong.json dqn_pong enjoy@data/dqn_cartpole_2018_12_02_124127/dqn_cartpole_t0_s0_ckptbestend_val in enjoy mode#242 Atari benchmark had been failing, but the root cause had finally been discovered and fix: wrong image preprocessing. This can be due to several factors, and we are doing ablation studies to check against the old code: - Image normalization cause the input values to be lowered by ~255, and the resultant loss is too small for optimizer.
PR #242 introduces:
env/wrapper.pyTransformImage to do the proper image transform: grayscale, downsize, and shape from w,h,c to PyTorch format c,h,wFrameStack which uses LazyFrames for efficiency to replace the agent-specific Atari stack frame preprocessing. This simplifies the Atari memoriesTuned parameters will be obtained and released next version.
Attached is a quick training curve on Pong, DQN, where the solution avg is +18:
#222 #224
OnPolicyImageReplay and ImageReplay memories#223 #225
#221
train@ mode#170
Fix the long standing pytorch + distributed using spawn multiprocessing due to Lab classes not pickleable. Just let the class wrapped in a mp_runner passed as mp.Process(target=mp_runner, args) so the classes don't get cloned from memory when spawning process, since it is now passed from outside.
#169 DQN target network replacement was in the wrong direction. Fix that.
#170 #171
Add a quick AtariPrioritizedReplay via some multi-inheritance black magic with PrioritizedReplay, AtariReplay
This release optimizes the RAM consumption and memory sampling speed after stress-testing with Atari. RAM growth is curbed, and replay memory RAM usage is now near theoretical optimality.
Thanks to @mwcvitkovic for providing major help with this release.
#163
add_single. This changes the API.body.df to track data efficiently as a replacement. This is the API replacement for above.#163 first optimization, halves replay RAM
float16 to accommodate big memory size. half a million max_size virtual memory goes from 200GB to 50GBfast_uniform_sampling to speed up#165 second optimization, halves replay RAM again to the theoretical minimum
next_states for replay memories due to redundancyself.latest_next_states during sampling#164
pip install -e . or python setup.py installdefault.json creation on first installThis major v2.0.0 release addresses the user feedbacks on usability and feature requests:
Note that this release is backward-incompatible with v1.x. and earlier.
v2.0.0: make components independent of the framework so it can be used outside of SLM-Lab for development and production, and improve usability. Backward-incompatible with v1.x.
#153
Session in lab.space_{method} to handle the space logic. Uses the SpaceSession in lab.post_body_init()#153
Net.cuda_id for device assignment (per network basis), and auto-calculate the cuda_id by trial and session index to distribute jobs#153 #148
distributed key to meta specDistSession class which acts as the worker.Trial creates the global networks for agents, then passes to and spawns DistSession. Effectively, the semantics of a session changes from being a disjoint copy to being a training worker.#155
state = (state - mean) / std#153
save() and load() now include network optimizersset_manual_seed to utilStackReplay to ConcatReplay for clarityBaseEnv as base class to OpenAIEnv and UnityEnvThis release adds PPOSIL, fixes some small issues with continuous actions, and PPO ratio computation.
#145 Implement PPOSIL. Improve debug logging #143 add Arch installer thanks to @angel-ayala
#138 kill hanging processes of Electron for plotting #145 fix PPO wrong graph update sequence causing ratio to be 1. Fix continuous action output construction. add guards. #146 fix continuous actions and add full tests
Source code(tar.gz)This release adds some new implementations, and fixes some bugs from first benchmark runs.
#127 Self-Imitation Learning #128 Checkpointing for saving models #129 Dueling Networks
#132 GPU test-run fixes #133 fix ActorCritic family loss compute getting detached, and linux plotting issues, add SHA to generated specs
Source code(tar.gz)This release is research-ready.
Finish implementation of all canonical algorithms and components. All design is fully refactored and usable across components as suitable. This release is ready for research. Read the updated doc
SLM Lab implements most of the recent canonical algorithms and various extensions. These are used as the base of research.
code: slm_lab/agent/algorithm
Various algorithms are in fact extensions of some simpler ones, and they are implemented as such. This makes the code very concise.
Policy Gradient:
Value-based:
Below are the modular building blocks for the algorithms. They are designed to be general, and are reused extensively.
code: slm_lab/agent/memory
For on-policy algorithms (policy gradient):
For off-policy algorithms (value-based)
code: slm_lab/agent/net
These networks are usable for all algorithms.
code: slm_lab/agent/algorithm/policy_util.py
yarn analyze <dir>This is the first stable release of the lab, with the core API and features finalized.
Refer to the docs: Github Repo | Lab Documentation | Experiment Log Book
All the crucial features of the lab are stable and tested:
The first release includes the following algorithms, with more to come later.
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