High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features

Last update: Jan 01, 2023

Overview

CleanRL (Clean Implementation of RL Algorithms)

CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. The highlight features of CleanRL are:

📜 Single-file implementation
- Every detail about an algorithm is put into the algorithm's own file. It is therefore easier to fully understand an algorithm and do research with.
📊 Benchmarked Implementation (7+ algorithms and 34+ games at https://benchmark.cleanrl.dev)
📈 Tensorboard Logging
🪛 Local Reproducibility via Seeding
🎮 Videos of Gameplay Capturing
🧫 Experiment Management with Weights and Biases
💸 Cloud Integration with docker and AWS

Good luck have fun 🚀

Get started

Prerequisites:

Python 3.8+
Poetry

To run experiments locally, give the following a try:

git clone https://github.com/vwxyzjn/cleanrl.git && cd cleanrl
poetry install

# alternatively, you could use `poetry shell` and do
# `python run cleanrl/ppo.py`
poetry run python cleanrl/ppo.py \
    --seed 1 \
    --gym-id CartPole-v0 \
    --total-timesteps 50000

# open another temrminal and enter `cd cleanrl/cleanrl`
tensorboard --logdir runs

To use experiment tracking with wandb, run

wandb login # only required for the first time
poetry run python cleanrl/ppo.py \
    --seed 1 \
    --gym-id CartPole-v0 \
    --total-timesteps 50000 \
    --track \
    --wandb-project-name cleanrltest

To run training scripts in other games:

poetry shell

# classic control
python cleanrl/dqn.py --gym-id CartPole-v1
python cleanrl/ppo.py --gym-id CartPole-v1
python cleanrl/c51.py --gym-id CartPole-v1

# atari
poetry install -E atari
python cleanrl/dqn_atari.py --gym-id BreakoutNoFrameskip-v4
python cleanrl/c51_atari.py --gym-id BreakoutNoFrameskip-v4
python cleanrl/ppo_atari.py --gym-id BreakoutNoFrameskip-v4
python cleanrl/apex_dqn_atari.py --gym-id BreakoutNoFrameskip-v4

# pybullet
poetry install -E pybullet
python cleanrl/td3_continuous_action.py --gym-id MinitaurBulletDuckEnv-v0
python cleanrl/ddpg_continuous_action.py --gym-id MinitaurBulletDuckEnv-v0
python cleanrl/sac_continuous_action.py --gym-id MinitaurBulletDuckEnv-v0

# procgen
poetry install -E procgen
python cleanrl/ppo_procgen.py --gym-id starpilot
python cleanrl/ppo_procgen_impala_cnn.py --gym-id starpilot
python cleanrl/ppg_procgen.py --gym-id starpilot
python cleanrl/ppg_procgen_impala_cnn.py --gym-id starpilot

Algorithms Implemented

Deep Q-Learning (DQN)
- dqn.py
  - For discrete action space.
- dqn_atari.py
  - For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
Categorical DQN (C51)
- c51.py
  - For discrete action space.
- c51_atari.py
  - For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
- c51_atari_visual.py
  - Adds return and q-values visulization for dqn_atari.py.
Proximal Policy Gradient (PPO)
- All of the PPO implementations below are augmented with some code-level optimizations. See https://costa.sh/blog-the-32-implementation-details-of-ppo.html for more details
- ppo.py
  - For discrete action space.
- ppo_continuous_action.py
  - For continuous action space. Also implemented Mujoco-specific code-level optimizations
- ppo_atari.py
  - For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
Soft Actor Critic (SAC)
- sac_continuous_action.py
  - For continuous action space.
Deep Deterministic Policy Gradient (DDPG)
- ddpg_continuous_action.py
  - For continuous action space.
Twin Delayed Deep Deterministic Policy Gradient (TD3)
- td3_continuous_action.py
  - For continuous action space.
Apex Deep Q-Learning (Apex-DQN)
- apex_dqn_atari_visual.py
  - For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.

Open RL Benchmark

Open RL Benchmark by CleanRL is a comprehensive, interactive and reproducible benchmark of deep Reinforcement Learning (RL) algorithms. It uses Weights and Biases to keep track of the experiment data of popular deep RL algorithms (e.g. DQN, PPO, DDPG, TD3) in a variety of games (e.g. Atari, Mujoco, PyBullet, Procgen, Griddly, MicroRTS). The experiment data includes:

reproducibility info:
- source code and requirements.txt
- hyper-parameters and the exact command to reproduce results
metrics:
- training metrics and videos of the agents playing the game
- system metrics and logs

Open RL Benchmark has over 1000+ experiments including runs from other projects, which is overwhelming to present in a single report. Instead, we present the results in separate reports. Please click on the links below to access them.

We hope it could bring a new level of transparency, openness, and reproducibility. Our plan is to benchmark as many algorithms and games as possible. If you are interested, please join us and contribute more algorithms and games. To get started, check out our contribution guide and our roadmap for the Open RL Benchmark

Cloud integration

Check out the documentation here

Support and get involved

We have a Discord Community for support. Feel free to ask questions. Posting in Github Issues and PRs are also welcome. Also our past video recordings are available at YouTube

Contribution

We have a short contribution guide here https://github.com/vwxyzjn/cleanrl/blob/master/CONTRIBUTING.md. Consider adding new algorithms or test new games on the Open RL Benchmark (https://benchmark.cleanrl.dev)

Big thanks to all the contributors of CleanRL!

References

I have been heavily inspired by the many repos and blog posts. Below contains a incomplete list of them.

The following ones helped me a lot with the continuous action space handling:

Citing CleanRL

If you use CleanRL in your work, please cite our technical paper:

@article{huang2021cleanrl,
    title={CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms}, 
    author={Shengyi Huang and Rousslan Fernand Julien Dossa and Chang Ye and Jeff Braga},
    year={2021},
    journal={arXiv preprint arXiv:2111.08819},
    url={https://arxiv.org/abs/2111.08819}
}

Comments

Add RPO to CleanRL
Description

Types of changes

[ ] Bug fix

[ ] New feature

[x] New algorithm

[ ] Documentation

Checklist:

[x] I've read the CONTRIBUTION guide (required).

[ ] I have ensured pre-commit run --all-files passes (required).

[ ] I have updated the documentation and previewed the changes via mkdocs serve.

[ ] I have updated the tests accordingly (if applicable).

If you are adding new algorithm variants or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[x ] I have contacted vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[ ] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[x] I have added additional documentation and previewed the changes via mkdocs serve.

[x] I have explained note-worthy implementation details.

[x] I have explained the logged metrics.

[x] I have added links to the original paper and related papers (if applicable).

[x] I have added links to the PR related to the algorithm variant.

[x] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[x] I have added the learning curves (in PNG format).

[x] I have added links to the tracked experiments.

[ ] I have updated the overview sections at the docs and the repo

[ ] I have updated the tests accordingly (if applicable).
opened by masud99r 24
Issues with applying PPO Impala on Retro Env in regards to running multiple environment

So what I essentially need is to so have something like "venv = ProcgenEnv(num_envs=" ... but for retro.make(). Running multiple retro environments is causing issues for me, and retrowrapper isn't helping. Thank you!

opened by hlsafin 20
Implement Gymnasium-compliant PPO script
Description

Types of changes

[ ] Bug fix

[x] New feature

[ ] New algorithm

[ ] Documentation

Checklist:

[x] I've read the CONTRIBUTION guide (required).

[x] I have ensured pre-commit run --all-files passes (required).

[ ] I have updated the documentation and previewed the changes via mkdocs serve.

[ ] I have updated the tests accordingly (if applicable).

If you are adding new algorithm variants or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[x] I have contacted vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[x] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[x] I have added additional documentation and previewed the changes via mkdocs serve.

[x] I have explained note-worthy implementation details.

[x] I have explained the logged metrics.

[x] I have added links to the original paper and related papers (if applicable).

[x] I have added links to the PR related to the algorithm variant.

[x] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[x] I have added the learning curves (in PNG format).

[x] I have added links to the tracked experiments.

[x] I have updated the overview sections at the docs and the repo

[x] I have updated the tests accordingly (if applicable).
opened by dtch1997 19
prototype jax with dqn
Description

JAX implementation for DQN Implementation for #220

Types of changes

[ ] Bug fix

[ ] New feature

[x] New algorithm

[x] Documentation

Checklist:

[x] I've read the CONTRIBUTION guide (required).

[x] I have ensured pre-commit run --all-files passes (required).

[x] I have updated the documentation and previewed the changes via mkdocs serve.

[x] I have updated the tests accordingly (if applicable).

If you are adding new algorithms or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[x] I have contacted @vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[x] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[x] I have added additional documentation and previewed the changes via mkdocs serve.

[x] I have explained note-worthy implementation details.

[x] I have explained the logged metrics.

[x] I have added links to the original paper and related papers (if applicable).

[x] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[x] I have added the learning curves (in PNG format with width=500 and height=300).

[x] I have added links to the tracked experiments.

[x] I have updated the tests accordingly (if applicable).
opened by kinalmehta 19

DDPG/TD3 target_actor output clip

Problem Description

Hi! It seems that the output of target_actor in DDPG/TD3 has been directly clipped to fit the action range boundaries, without multiplying by max_action. But in Fujimoto's DDPG/TD3 code[1] and some other implementations, the max_action has been add in the last tanh layer of the actor network, so they don't use clip. Have u ever tried the second implementation?

if global_step > args.learning_starts:
                data = rb.sample(args.batch_size)
                with torch.no_grad():
                    next_state_actions = (target_actor(data.next_observations)).clamp(
                        envs.single_action_space.low[0], envs.single_action_space.high[0]
                    )
                    qf1_next_target = qf1_target(data.next_observations, next_state_actions)
                    next_q_value = data.rewards.flatten() + (1 - data.dones.flatten()) * args.gamma * (qf1_next_target).view(-1)

[1] https://github.com/sfujim/TD3

opened by huxiao09 19

Using jax scan for PPO + atari + envpool XLA
Description

Modifying the code to use jax.lax.scan for fast compile time and small speed improvement.

The loss metrics of this pull request (blue) are consistent with the original version (green).

The performance is similar to the original with a slight speed improvement.

The command used is python cleanrl/ppo_atari_envpool_xla_jax_scan.py --env-id Breakout-v5 --total-timesteps 10000000 --num-envs 32 --seed 111 (blue) and python cleanrl/ppo_atari_envpool_xla_jax.py --env-id Breakout-v5 --total-timesteps 10000000 --num-envs 32 --seed 111 (green).

Types of changes

[ ] Bug fix

[x] New feature

[ ] New algorithm

[ ] Documentation

Checklist:

[x] I've read the CONTRIBUTION guide (required).

[x] I have ensured pre-commit run --all-files passes (required).

[x] I have updated the documentation and previewed the changes via mkdocs serve.

[x] I have updated the tests accordingly (if applicable).

If you are adding new algorithm variants or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[x] I have contacted vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[x] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[x] I have added additional documentation and previewed the changes via mkdocs serve.

[x] I have explained note-worthy implementation details.

[x] I have explained the logged metrics.

[x] I have added links to the original paper and related papers (if applicable).

[x] I have added links to the PR related to the algorithm variant.

[x] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[x] I have added the learning curves (in PNG format).

[x] I have added links to the tracked experiments.

[ ] I have updated the overview sections at the docs and the repo

[x] I have updated the tests accordingly (if applicable).
opened by 51616 17
ppo with timeout handling
Description

Closes #198

Types of changes

[ ] Bug fix

[x] New feature

[ ] New algorithm

[ ] Documentation

Checklist:

[x] I've read the CONTRIBUTION guide (required).

[x] I have ensured pre-commit run --all-files passes (required).

[ ] I have updated the documentation and previewed the changes via mkdocs serve.

[ ] I have updated the tests accordingly (if applicable).

If you are adding new algorithms or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[ ] I have contacted @vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[ ] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[ ] I have added additional documentation and previewed the changes via mkdocs serve.

[ ] I have explained note-worthy implementation details.

[ ] I have explained the logged metrics.

[ ] I have added links to the original paper and related papers (if applicable).

[ ] I have added links to the PR related to the algorithm.

[ ] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[ ] I have added the learning curves (in PNG format with width=500 and height=300).

[ ] I have added links to the tracked experiments.

[ ] I have updated the tests accordingly (if applicable).
opened by Howuhh 13
Documentation Site

Problem Description

Although CleanRL generally has a simplistic implementation, it will be desirable to have a documentation site for some situations. For example, I'm not sure where to put instructions on how to do start and resume with CleanRL's scripts. See #33, #14.

opened by vwxyzjn 13
Add PPO Atari LSTM example

Referenced resources:

Illustrated Guide to LSTM's and GRU's: A step by step explanation

openai/baselines: Initialize the hidden states of LSTM to be zeros

openai/baselines: Mask out observations at the end of episode

Takes about ~40 lines of code to implement (see diff here) and the pilot run is at here

I guess the only thing is here we use LSTM and frame stack at the same time....

opened by vwxyzjn 12
TD3: fixed dimension of clipped_noise for target actions, added noise …
Description

Closes #279.

td3_continuous_action.py: noise sampled to compute the Q network update target matches action dimensions in the buffer

td3_continuous_action.py: aforementioned noise is also scaled to match the scaling range of the actions.

Types of changes

[x] Bug fix

[ ] New feature

[ ] New algorithm

[ ] Documentation

Checklist:

[x] I've read the CONTRIBUTION guide (required).

[x] I have ensured pre-commit run --all-files passes (required).

~~[ ] I have updated the documentation and previewed the changes via mkdocs serve.~~

[x] I have updated the tests accordingly (if applicable).

If you are adding new algorithms or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[x] I have contacted vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[x] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[x] I have added additional documentation and previewed the changes via mkdocs serve.

[x] I have explained note-worthy implementation details.

[x] I have explained the logged metrics.

[x] I have added links to the original paper and related papers (if applicable).

[x] I have added links to the PR related to the algorithm.

[x] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[x] I have added the learning curves (in PNG format with width=500 and height=300).

[x] I have added links to the tracked experiments.

[x] I have updated the overview sections at the docs and the repo

[x] I have updated the tests accordingly (if applicable).
opened by dosssman 11

Hyperparameter optimization

Description

This PR adds a first pass of hyperparameter optimization.

The API design roughly looks like

import optuna
from cleanrl_utils.tuner import Tuner
    
tuner = Tuner(
    script="cleanrl/ppo.py",
    metric="charts/episodic_return",
    metric_last_n_average_window=50,
    direction="maximize",
    target_scores={
        "CartPole-v1": [0, 500],
        "Acrobot-v1": [-500, 0],
    },
    params_fn=lambda trial: {
        "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003),
        "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]),
        "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4]),
        "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]),
        "vf-coef": trial.suggest_uniform("vf-coef", 0, 5),
        "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5),
        "total-timesteps": 10000,
        "num-envs": 16,
    },
    pruner=optuna.pruners.MedianPruner(n_startup_trials=5),
    wandb_kwargs={"project": "cleanrl"},
)
tuner.tune(
    num_trials=10,
    num_seeds=3,
)

Preliminary docs are available at https://cleanrl-jlu83xh5n-vwxyzjn.vercel.app/advanced/hyperparameter-tuning/

Types of changes

[ ] Bug fix
[x] New feature
[ ] New algorithm
[x] Documentation

Checklist:

[x] I've read the CONTRIBUTION guide (required).
[x] I have ensured pre-commit run --all-files passes (required).
[x] I have updated the documentation and previewed the changes via mkdocs serve.
[x] I have updated the tests accordingly (if applicable).

opened by vwxyzjn 11

Deprecate `ppo_procgen.py` in favor of EnvPool

Problem Description

Given the EnvPool==0.8.0 release by @YukunJ, @LeoGuo98, @Trinkle23897 (https://github.com/sail-sg/envpool/pull/197), we can go ahead and deprecate ppo_procgen.py in favor of #338, which should also work with procgen but gives us the benefit of JAX, EnvPool's Async API, and a more concise codebase.

opened by vwxyzjn 1
PPO with machado preprocessing
Description

Types of changes

[ ] Bug fix

[ ] New feature

[ ] New algorithm

[ ] Documentation

Checklist:

[ ] I've read the CONTRIBUTION guide (required).

[ ] I have ensured pre-commit run --all-files passes (required).

[ ] I have updated the documentation and previewed the changes via mkdocs serve.

[ ] I have updated the tests accordingly (if applicable).

If you are adding new algorithm variants or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[ ] I have contacted vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[ ] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[ ] I have added additional documentation and previewed the changes via mkdocs serve.

[ ] I have explained note-worthy implementation details.

[ ] I have explained the logged metrics.

[ ] I have added links to the original paper and related papers (if applicable).

[ ] I have added links to the PR related to the algorithm variant.

[ ] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[ ] I have added the learning curves (in PNG format).

[ ] I have added links to the tracked experiments.

[ ] I have updated the overview sections at the docs and the repo

[ ] I have updated the tests accordingly (if applicable).
opened by vwxyzjn 3
What is the reason for returning mean in SAC get_action function if it's never used?
Problem Description

In the script sac_continuous_action.py, the get_action function in the Actor class returns action, log_prob, mean. action and log_prob is used but mean is never used. Is there a reason to return that value when it's never used in the code? As a new comer it's a little confusing on why that is needed.

Checklist

[x] I have installed dependencies via poetry install (see CleanRL's installation guideline.

[x] I have checked that there is no similar issue in the repo.

[x] I have checked the documentation site and found not relevant information in GitHub issues.

Current Behavior

Works as expected

Expected Behavior

Works as expected

Possible Solution

Remove the mean returned in the get_action function in Actor class
opened by sudonymously 0
Cleanrl for MARL

Contribution to MARL

I would like to contribute to Cleanrl repo by extending RL algorithms to Multi-Agent Systems (i.e MARL). I have discussed the same with @vwxyzjn, and he suggested starting an issue here. If anyone is interested in contributing to MARL, please respond here. Going forward, we can lay out the roadmap and share the responsibilities.

Thank you.

opened by vbaddam 8
Torchx integration
Description

Our current cloud integration is pretty hacky. I haven't seen anyone used it and it has been a maintenance burden for us. Using a more managed utility to launch experiments in the cloud is desirable. There are two primary contenders and their pros and cons:

torchx

✅ support for slurm

✅ support for running tasks locally

✅ the docker image is automatically pushed with a hash for AWS Batch

❌ still need to spin up cloud resources (e.g., aws batch), which is complicated but can be mitigated by using terraform

skypilot

✅ support for managing spot instances and auto resume them

✅ compare pricing

✅ debuggability via sky ssh mycluster

✅ good for folks who don't always have a GPU machine

❌ need to wait for the clusters to be spun up

All of them:

✅ support for aws, gcp, azure

This PR

Better cloud integration utility by leveraging torchx. It should really be an elegant solution for us and has the following benefits:

we can deprecate our cloud utilities and release ourselves from their maintenance burden

support for slurm, kubernetes, aws batch, gcp (https://github.com/pytorch/torchx/issues/410#issuecomment-1301186265) and others

Give it a try by running

poetry run torchx run --scheduler local_docker utils.python --gpu 1 --script cleanrl/cleanrl.py poetry run torchx run --scheduler aws_batch --scheduler_args queue=c5a-large,image_repo=vwxyzjn/cleanrl utils.python --script cleanrl/ppo.py poetry run torchx status aws_batch://torchx/c5a-large:torchx_utils_python-pn9sx3wzq0qcwd

Types of changes

[ ] Bug fix

[x] New feature

[ ] New algorithm

[ ] Documentation

Checklist:

[ ] I've read the CONTRIBUTION guide (required).

[ ] I have ensured pre-commit run --all-files passes (required).

[ ] I have updated the documentation and previewed the changes via mkdocs serve.

[ ] I have updated the tests accordingly (if applicable).

If you are adding new algorithm variants or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[ ] I have contacted vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[ ] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[ ] I have added additional documentation and previewed the changes via mkdocs serve.

[ ] I have explained note-worthy implementation details.

[ ] I have explained the logged metrics.

[ ] I have added links to the original paper and related papers (if applicable).

[ ] I have added links to the PR related to the algorithm variant.

[ ] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[ ] I have added the learning curves (in PNG format).

[ ] I have added links to the tracked experiments.

[ ] I have updated the overview sections at the docs and the repo

[ ] I have updated the tests accordingly (if applicable).
opened by vwxyzjn 1
Brax + PPO integration
Description

Test out integration with brax. It seems to work out of the box without having to implement observation normalization — https://wandb.ai/costa-huang/cleanRL/runs/2aemjwey?workspace=user-costa-huang

Compilation takes ~400 seconds, and getting 6000 rewards in Ant takes about 100 seconds with GPU. In comparison, the official demo takes 30 seconds to compile and about 80 seconds to reach ~8000 rewards (using TPU I presume). Our compilation time takes significantly longer, most likely because we didn't use lax.scan or jax.foriloop, but once the compilation finished the SPS is about 600k.

CC @joaogui1

Types of changes

[ ] Bug fix

[ ] New feature

[x] New algorithm

[ ] Documentation

Checklist:

[ ] I've read the CONTRIBUTION guide (required).

[ ] I have ensured pre-commit run --all-files passes (required).

[ ] I have updated the documentation and previewed the changes via mkdocs serve.

[ ] I have updated the tests accordingly (if applicable).

If you are adding new algorithms or your change could result in performance difference, you may need to (re-)run tracked experiments. See https://github.com/vwxyzjn/cleanrl/pull/137 as an example PR.

[ ] I have contacted vwxyzjn to obtain access to the openrlbenchmark W&B team (required).

[ ] I have tracked applicable experiments in openrlbenchmark/cleanrl with --capture-video flag toggled on (required).

[ ] I have added additional documentation and previewed the changes via mkdocs serve.

[ ] I have explained note-worthy implementation details.

[ ] I have explained the logged metrics.

[ ] I have added links to the original paper and related papers (if applicable).

[ ] I have added links to the PR related to the algorithm.

[ ] I have created a table comparing my results against those from reputable sources (i.e., the original paper or other reference implementation).

[ ] I have added the learning curves (in PNG format with width=500 and height=300).

[ ] I have added links to the tracked experiments.

[ ] I have updated the overview sections at the docs and the repo

[ ] I have updated the tests accordingly (if applicable).
opened by vwxyzjn 1

Releases(v1.0.0)

v1.0.0(Nov 14, 2022)
🎉 We are thrilled to announce the v1.0.0 CleanRL Release. Along with our CleanRL paper's recent publication in Journal of Machine Learning Research, our v1.0.0 release includes reworked documentation, new algorithm variants, support for google's new ML framework JAX, hyperparameter tuning utilities, and more. CleanRL has come a long way making high-quality deep reinforcement learning implementations easy to understand and reproducible. This release is a major milestone for the project and we are excited to share it with you. Over 90 PRs were merged to make this release possible. We would like to thank all the contributors who made this release possible.

Reworked documentation

One of the biggest change of the v1 release is the added documentation at docs.cleanrl.dev. Having great documentation is important for building a reliable and reproducible project. We have reworked the documentation to make it easier to understand and use. For each implemented algorithm, we have documented as much as we can to promote transparency:

Short description of the algorithm and references

A list of implemented variant

The usage information

The explanation of the logged metrics

The documentation of implementation details

Experimental results

Here is a list of the algorithm variants and their documentation:

| Algorithm | Variants Implemented | | ----------- | ----------- | | ✅ Proximal Policy Gradient (PPO) | ppo.py, docs | | | ppo_atari.py, docs | | ppo_continuous_action.py, docs | | ppo_atari_lstm.py, docs | | ppo_atari_envpool.py, docs | | ppo_atari_envpool_xla_jax.py, docs | | ppo_procgen.py, docs | | ppo_atari_multigpu.py, docs | | ppo_pettingzoo_ma_atari.py, docs | | ppo_continuous_action_isaacgym.py, docs | ✅ Deep Q-Learning (DQN) | dqn.py, docs | | | dqn_atari.py, docs | | | dqn_jax.py, docs | | | dqn_atari_jax.py, docs | | ✅ Categorical DQN (C51) | c51.py, docs | | | c51_atari.py, docs | | ✅ Soft Actor-Critic (SAC) | sac_continuous_action.py, docs | | ✅ Deep Deterministic Policy Gradient (DDPG) | ddpg_continuous_action.py, docs | | | ddpg_continuous_action_jax.py, docs | ✅ Twin Delayed Deep Deterministic Policy Gradient (TD3) | td3_continuous_action.py, docs | | | td3_continuous_action_jax.py, docs | | ✅ Phasic Policy Gradient (PPG) | ppg_procgen.py, docs | | ✅ Random Network Distillation (RND) | ppo_rnd_envpool.py, docs |

We also improved the contribution guide to make it easier for new contributors to get started. We are still working on improving the documentation. If you have any suggestions, please let us know in the GitHub Issues.

New algorithm variants, support for JAX

We now support JAX-based learning algorithm variants, which are usually faster than the torch equivalent! Here are the docs of the new JAX-based DQN, TD3, and DDPG implementations:

dqn_atari_jax.py @kinalmehta in vwxyzjn/cleanrl#222

about 25% faster than dqn_atari.py.

td3_continuous_action_jax.py by @joaogui1 in vwxyzjn/cleanrl#225

about 2.5-4x faster than td3_continuous_action.py.

ddpg_continuous_action_jax.py by @vwxyzjn in vwxyzjn/cleanrl#187

about 2.5-4x faster than ddpg_continuous_action.py.

ppo_atari_envpool_xla_jax.py by @vwxyzjn in vwxyzjn/cleanrl#227

about 3x faster than openai/baselines' PPO.

For example, below are the benchmark of DDPG + JAX (see docs here for further detail):

Other new algorithm variants include multi-GPU PPO, PPO prototype that works with Isaac Gym, multi-agent Atari PPO, and refactored PPG and PPO-RND implementations:

ppo_atari_multigpu.pu by @vwxyzjn in vwxyzjn/cleanrl#178

about 34% faster than ppo_atari.py which uses SyncVectorEnv.

ppo_continuous_action_isaacgym.py by @vwxyzjn in vwxyzjn/cleanrl#233

achieves 4000+ score and 30M steps on IsaacGymEnvs' Ant in 4 mins.

ppo_pettingzoo_ma_atari.py by @vwxyzjn in vwxyzjn/cleanrl#188

achieves ~4000 episodic length (not episodic return) in Pong, creating competitive self play agents.

ppg_procgen.py by @Dipamc77 in vwxyzjn/cleanrl#186

matches openai/baselines' PPO performance in StarPilot (easy), BossFight (easy), and BigFish (easy).

ppo_rnd_envpoolpy.py by @yooceii in vwxyzjn/cleanrl#151

achieves ~7100 in MontezumaRevengeNoFrameSkip-v4.

Tooling improvements

We love tools! The v1.0.0 release comes with a series of DevOps improvements, including pre-commit utilities, CI integration with GitHub to run end-to-end test cases. We also make available a new hyperparameter tuning tool and a new tool for running benchmark experiments.

DevOps

We added a pre-commit utility to help contributors to format their code, check for spelling, and removing unused variables and imports before submitting a pull request (see Contribution guide for more detail).

To ensure our single-file implementations can run without error, we also added CI/CD pipeline which now runs end-to-end test cases for all the algorithm variants. The pipeline also tests builds across different operating systems, such as Linux, macOS, and Windows (see here as an example). GitHub actions are free for open source projects, and we are very happy to have this tool to help us maintain the project.

Hyperparameter tuning utilities

We now have preliminary support for hyperparameter tuning via optuna (see docs), which is designed to help researchers to find a single set of hyperparameters that work well with a kind of games. The current API looks like below:

import optuna from cleanrl_utils.tuner import Tuner tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", aggregation_type="average", target_scores={ "CartPole-v1": [0, 500], "Acrobot-v1": [-500, 0], }, params_fn=lambda trial: { "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4, 8]), "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), "total-timesteps": 100000, "num-envs": 16, }, pruner=optuna.pruners.MedianPruner(n_startup_trials=5), sampler=optuna.samplers.TPESampler(), ) tuner.tune( num_trials=100, num_seeds=3, )

Benchmarking utilities

We also added a new tool for running benchmark experiments. The tool is designed to help researchers to quickly run benchmark experiments across different algorithms environments with some random seeds. The tool lives in the cleanrl_utils.benchmark module, and the users can run commands such as:

OMP_NUM_THREADS=1 xvfb-run -a python -m cleanrl_utils.benchmark \ --env-ids CartPole-v1 Acrobot-v1 MountainCar-v0 \ --command "poetry run python cleanrl/ppo.py --cuda False --track --capture-video" \ --num-seeds 3 \ --workers 5

which will run the ppo.py script with --cuda False --track --capture-video arguments across 3 random seeds for 3 environments. It uses multiprocessing to create a pool of 5 workers run the experiments in parallel.

What’s next?

It is an exciting time and new improvements are coming to CleanRL. We plan to add more JAX-based implementations, huggingface integration, some RLops prototypes, and support Gymnasium. CleanRL is a community-based project and we always welcome new contributors. If there is an algorithm or new feature you would like to contribute, feel free to chat with us on our discord channel or raise a GitHub issue.

More JAX implementations

More JAX-based implementation are coming. Antonin Raffin, the core maintainer of Stable-baselines3, SBX, and rl-baselines3-zoo, is contributing an optimized Soft Actor Critic implementation in JAX (vwxyzjn/cleanrl#300) and TD3+TQC, and DroQ (vwxyzjn/cleanrl#272. These are incredibly exciting new algorithms. For example, DroQ is extremely sample effcient and can obtain ~5000 return in HalfCheetah-v3 in just 100k steps (tracked sbx experiment).

Huggingface integration

Huggingface Hub 🤗 is a great platform for sharing and collaborating models. We are working on a new integration with Huggingface Hub to make it easier for researchers to share their RL models and benchmark them against other models (vwxyzjn/cleanrl#292). Stay tuned! In the future, we will have a simple snippet for loading models like below:

import random from typing import Callable import gym import numpy as np import torch def evaluate( model_path: str, make_env: Callable, env_id: str, eval_episodes: int, run_name: str, Model: torch.nn.Module, device: torch.device, epsilon: float = 0.05, capture_video: bool = True, ): envs = gym.vector.SyncVectorEnv([make_env(env_id, 0, 0, capture_video, run_name)]) model = Model(envs).to(device) model.load_state_dict(torch.load(model_path)) model.eval() obs = envs.reset() episodic_returns = [] while len(episodic_returns) < eval_episodes: if random.random() < epsilon: actions = np.array([envs.single_action_space.sample() for _ in range(envs.num_envs)]) else: q_values = model(torch.Tensor(obs).to(device)) actions = torch.argmax(q_values, dim=1).cpu().numpy() next_obs, _, _, infos = envs.step(actions) for info in infos: if "episode" in info.keys(): print(f"eval_episode={len(episodic_returns)}, episodic_return={info['episode']['r']}") episodic_returns += [info["episode"]["r"]] obs = next_obs return episodic_returns if __name__ == "__main__": from huggingface_hub import hf_hub_download from cleanrl.dqn import QNetwork, make_env model_path = hf_hub_download(repo_id="cleanrl/CartPole-v1-dqn-seed1", filename="q_network.pth")

RLops

How do we know the effect of a new feature / bug fix? DRL is brittle and has a series of reproducibility issues — even bug fixes sometimes could introduce performance regression (e.g., see how a bug fix of contact force in MuJoCo results in worse performance for PPO). Therefore, it is essential to understand how the proposed changes impact the performance of the algorithms.

We are working a prototype tool that allows us to compare the performance of the library at different versions of the tracked experiment (vwxyzjn/cleanrl#307). With this tool, we can confidently merge new features / bug fixes without worrying about introducing catastrophic regression. The users can run commands such as:

python -m cleanrl_utils.rlops --exp-name ddpg_continuous_action \ --wandb-project-name cleanrl \ --wandb-entity openrlbenchmark \ --tags 'pr-299' 'rlops-pilot' \ --env-ids HalfCheetah-v2 Walker2d-v2 Hopper-v2 InvertedPendulum-v2 Humanoid-v2 Pusher-v2 \ --output-filename compare.png \ --scan-history \ --metric-last-n-average-window 100 \ --report

which generates the following image

Support for Gymnasium

Farama-Foundation/Gymnasium is the next generation of openai/gym that will continue to be maintained and introduce new features. Please see their announcement for further detail. We are migrating to gymnasium and the progress can be tracked in vwxyzjn/cleanrl#277.

Today we're launching the Farama Foundation, a new nonprofit dedicated to open source reinforcement learning, and we're beginning by maintaining and standardizing all the major open source reinforcement learning environments. Read more here: https://t.co/kQqFMQdVqn
— Farama Foundation (@FaramaFound) October 25, 2022

Also, the Farama foundation is working a project called Shimmy which offers conversion wrapper for deepmind/dm_env environments, such as dm_control and deepmind/lab. This is an exciting project that will allow us to support deepmind/dm_env in the future.

Contributions

CleanRL has benefited from the contributions of many awesome folks. I would like to cordially thank the core dev members @dosssman @yooceii @Dipamc @kinalmehta @bragajj for their efforts in helping maintain the CleanRL repository. I would also like to give a shout-out to our new contributors @cool-RR, @Howuhh, @jseppanen, @joaogui1, @ALPH2H, @ElliotMunro200, @WillDudley, and @sdpkjc.

We always welcome new contributors to the project. If you are interested in contributing to CleanRL (e.g., new features, bug fixes, new algorithms), please check out our reworked contributing guide.

New CleanRL Supported Publications

Md Masudur Rahman and Yexiang Xue. "Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning." In Proceedings of the IEEE International Conference on Machine Learning and Applications (ICMLA), 2022. https://arxiv.org/pdf/2210.07312.pdf

Weng, Jiayi, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu et al. "Envpool: A highly parallel reinforcement learning environment execution engine." In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=BubxnHpuMbG

Huang, Shengyi, Rousslan Fernand Julien Dossa, Antonin Raffin, Anssi Kanervisto, and Weixun Wang. "The 37 Implementation Details of Proximal Policy Optimization." International Conference on Learning Representations 2022 Blog Post Track, https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/

Huang, Shengyi, and Santiago Ontañón. "A closer look at invalid action masking in policy gradient algorithms." The International FLAIRS Conference Proceedings, 35. https://journals.flvc.org/FLAIRS/article/view/130584

Schmidt, Dominik, and Thomas Schmied. "Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari." Deep Reinforcement Learning Workshop at the 35th Conference on Neural Information Processing Systems, https://arxiv.org/abs/2111.10247

New Features PR

prototype jax with ddpg by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/187

Isaac Gym Envs PPO updates by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/233

JAX TD3 prototype by @joaogui1 in https://github.com/vwxyzjn/cleanrl/pull/225

prototype jax with dqn by @kinalmehta in https://github.com/vwxyzjn/cleanrl/pull/222

Poetry 1.2 by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/271

Add rnd_ppo.py documentation and refactor by @yooceii in https://github.com/vwxyzjn/cleanrl/pull/151

Hyperparameter optimization by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/228

Update the hyperparameter optimization example script by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/268

Export requirements.txt automatically by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/143

Auto-upgrade syntax via pyupgrade by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/158

Introduce benchmark utilities by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/165

Match PPG implementation by @Dipamc77 in https://github.com/vwxyzjn/cleanrl/pull/186

See the documentation here: https://docs.cleanrl.dev/rl-algorithms/ppg/

Proper multi-gpu support with PPO by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/178

See the documentation here: https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_multigpupy

Support Pettingzoo Multi-agent Atari envs with PPO by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/188

See the documentation here: https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_pettingzoo_ma_ataripy# Bug Fixes PR

Bug fix and refacotring PR

Let ppo_continuous_action.pyonly run 1M steps by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/161

Change ppo.py's default timesteps by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/164

Enable video recording for ppo_procgen.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/166

Refactor replay based scripts by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/173

Td3 ddpg action bound fix by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/211

added gamma to reward normalization wrappers by @Howuhh in https://github.com/vwxyzjn/cleanrl/pull/209

Seed envpool environment explicitly by @jseppanen in https://github.com/vwxyzjn/cleanrl/pull/238

Fix PPO + Isaac Gym Benchmark Script by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/243

Fix for noise sampling for the TD3 exploration by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/260

Documentation PR

Add a note on PPG's performance by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/199

Clarify CleanRL is a non-modular library by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/200

Fix documentation link by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/213

JAX + DDPG docs fix by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/229

Fix links in docs for ppo_continuous_action_isaacgym.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/242

Fix docs (badge, TD3 + JAX, and DQN + JAX) by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/246

Fix typos by @ALPH2H in https://github.com/vwxyzjn/cleanrl/pull/282

Fix docs links in README.md by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/254

chore: remove unused parameters in jax implementations by @kinalmehta in https://github.com/vwxyzjn/cleanrl/pull/264

Add ddpg_continuous_action.py docs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/137

Fix DDPG docs' description by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/139

Fix typo in DDPG docs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/140

Fix incorrect links in the DDPG docs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/142

DDPG documnetation tweaks; added Q loss equations and light explanation by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/145

Add dqn_atari.py documentation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/124

Add documentation for td3_continuous_action.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/141

SAC Documentation - Benchmarks - Minor code tweaks by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/146

Add docs for c51.py and c51_atari.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/159

Add docs for dqn.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/157

Address stale documentation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/169

Documentation improvement - fix links and mkdocs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/181

Improve documentation and contribution guide by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/189

Fix documentation links in README.md by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/192

Fix the implemented varaints section in PPO by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/193

Misc PR

Show correct exception cause by @cool-RR in https://github.com/vwxyzjn/cleanrl/pull/205

Remove pettingzoo's pistonball example by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/214

Leverage CI to speed up poetry lock by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/235

Ubuntu runner for poetry lock by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/236

Remove the github pages CI in favor of vercel by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/241

Clarify LICENSE info by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/253

Update published paper citation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/284

Refactor dqn word choice by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/257

Add Pull Request template by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/122

Amend license to give proper attribution by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/152

Introduce better contribution guide by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/154

Fix the default wandb project name in ppo_atari_envpool.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/160

Removes unmaintained scripts by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/170

Add PPO documentation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/163

Add docs header by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/174

Update README.md by @ElliotMunro200 in https://github.com/vwxyzjn/cleanrl/pull/177

Update issue_template.md by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/180

Temporarily Remove PPO-RND by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/190 .

Contributors

@ElliotMunro200 made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/177

@dipamc made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/186

@cool-RR made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/205

@Howuhh made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/209

@jseppanen made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/238

@joaogui1 made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/225

@kinalmehta made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/222

@ALPH2H made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/282

@WillDudley made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/302

@sdpkjc made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/299

@masud99r made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/316

Full Changelog: https://github.com/vwxyzjn/cleanrl/compare/v0.6.0...v1.0.0
Source code(tar.gz)
Source code(zip)
v1.0.0b2(Oct 3, 2022)
🎉 I am thrilled to announce the v1.0.0b2 CleanRL Beta Release. This new release comes with exciting new features. First, we now support JAX-based learning algorithms, which are usually faster than the torch equivalent! Here are the docs of the new JAX-based DQN, TD3, and DDPG implementations:

dqn_atari_jax.py

dqn_jax.py

td3_continuous_action_jax.py

ddpg_continuous_action_jax.py

Also, we now have preliminary support for hyperparameter tuning via optuna (see docs), which is designed to help researchers to find a single set of hyperparameters that work well with a kind of games. The current API looks like below:

import optuna from cleanrl_utils.tuner import Tuner tuner = Tuner( script="cleanrl/ppo.py", metric="charts/episodic_return", metric_last_n_average_window=50, direction="maximize", aggregation_type="average", target_scores={ "CartPole-v1": [0, 500], "Acrobot-v1": [-500, 0], }, params_fn=lambda trial: { "learning-rate": trial.suggest_loguniform("learning-rate", 0.0003, 0.003), "num-minibatches": trial.suggest_categorical("num-minibatches", [1, 2, 4]), "update-epochs": trial.suggest_categorical("update-epochs", [1, 2, 4, 8]), "num-steps": trial.suggest_categorical("num-steps", [5, 16, 32, 64, 128]), "vf-coef": trial.suggest_uniform("vf-coef", 0, 5), "max-grad-norm": trial.suggest_uniform("max-grad-norm", 0, 5), "total-timesteps": 100000, "num-envs": 16, }, pruner=optuna.pruners.MedianPruner(n_startup_trials=5), sampler=optuna.samplers.TPESampler(), ) tuner.tune( num_trials=100, num_seeds=3, )

Besides, we added support for new algorithms/environments, which are

Isaac Gym support in PPO for GPU accelerated robotics environment. ppo_continuous_action_isaacgym.py

Random Network Distillation (RND) for highly exploratory environments: ppo_rnd_envpool.py

I would like to cordially thank the core dev members @dosssman @yooceii @Dipamc @kinalmehta for their efforts in helping maintain the CleanRL repository. I would also like to give a shout-out to our new contributors @cool-RR, @Howuhh, @jseppanen, @joaogui1, @kinalmehta, and @ALPH2H.

New CleanRL Supported Publications

Jiayi Weng, Min Lin, Shengyi Huang, Bo Liu, Denys Makoviichuk, Viktor Makoviychuk, Zichen Liu, Yufan Song, Ting Luo, Yukun Jiang, Zhongwen Xu, & Shuicheng YAN (2022). EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=BubxnHpuMbG

New Features PR

prototype jax with ddpg by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/187

Isaac Gym Envs PPO updates by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/233

JAX TD3 prototype by @joaogui1 in https://github.com/vwxyzjn/cleanrl/pull/225

prototype jax with dqn by @kinalmehta in https://github.com/vwxyzjn/cleanrl/pull/222

Poetry 1.2 by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/271

Add rnd_ppo.py documentation and refactor by @yooceii in https://github.com/vwxyzjn/cleanrl/pull/151

Hyperparameter optimization by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/228

Update the hyperparameter optimization example script by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/268

Bug Fixes PR

Td3 ddpg action bound fix by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/211

added gamma to reward normalization wrappers by @Howuhh in https://github.com/vwxyzjn/cleanrl/pull/209

Seed envpool environment explicitly by @jseppanen in https://github.com/vwxyzjn/cleanrl/pull/238

Fix PPO + Isaac Gym Benchmark Script by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/243

Fix for noise sampling for the TD3 exploration by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/260

Documentation PR

Add a note on PPG's performance by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/199

Clarify CleanRL is a non-modular library by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/200

Fix documentation link by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/213

JAX + DDPG docs fix by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/229

Fix links in docs for ppo_continuous_action_isaacgym.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/242

Fix docs (badge, TD3 + JAX, and DQN + JAX) by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/246

Fix typos by @ALPH2H in https://github.com/vwxyzjn/cleanrl/pull/282

Fix docs links in README.md by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/254

chore: remove unused parameters in jax implementations by @kinalmehta in https://github.com/vwxyzjn/cleanrl/pull/264

Misc PR

Show correct exception cause by @cool-RR in https://github.com/vwxyzjn/cleanrl/pull/205

Remove pettingzoo's pistonball example by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/214

Leverage CI to speed up poetry lock by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/235

Ubuntu runner for poetry lock by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/236

Remove the github pages CI in favor of vercel by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/241

Clarify LICENSE info by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/253

Update published paper citation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/284

Refactor dqn word choice by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/257

New Contributors

@cool-RR made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/205

@Howuhh made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/209

@jseppanen made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/238

@joaogui1 made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/225

@kinalmehta made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/222

@ALPH2H made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/282

Full Changelog: https://github.com/vwxyzjn/cleanrl/compare/v1.0.0b1...v1.0.0b2
Source code(tar.gz)
Source code(zip)
v1.0.0b1(Jun 7, 2022)
🎉 I am thrilled to announce the v1.0.0b1 CleanRL Beta Release. CleanRL has come a long way making high-quality deep reinforcement learning implementations easy to understand. In this release, we have put a huge effort into revamping our documentation site, making our implementation friendly to use for new users.

I would like to cordially thank the core dev members @dosssman @yooceii @Dipamc77 @bragajj for their efforts in helping maintain the CleanRL repository. I would also like to give a shout-out to our new contributors @ElliotMunro200 and @Dipamc77.

New CleanRL supported publications

Huang, S., Dossa, R., Raffin, A., Kanervisto, A., & Wang, W. (2022). The 37 Implementation Details of Proximal Policy Optimization, International Conference on Learning Representations 2022 Blog Post Track

Huang, S., Ontañón, S., (2022). A Closer Look at Invalid Action Masking in Policy Gradient Algorithms, The International FLAIRS Conference Proceedings, 35.

Schmidt, D., & Schmied, T. (2021). Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari Deep Reinforcement Learning Workshop at the 35th Conference on Neural Information Processing Systems

New algorithm variants

Match PPG implementation by @Dipamc77 in https://github.com/vwxyzjn/cleanrl/pull/186

See the documentation here: https://docs.cleanrl.dev/rl-algorithms/ppg/

Proper multi-gpu support with PPO by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/178

See the documentation here: https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_atari_multigpupy

Support Pettingzoo Multi-agent Atari envs with PPO by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/188

See the documentation here: https://docs.cleanrl.dev/rl-algorithms/ppo/#ppo_pettingzoo_ma_ataripy

Refactoring changes

Let ppo_continuous_action.pyonly run 1M steps by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/161

Change ppo.py's default timesteps by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/164

Enable video recording for ppo_procgen.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/166

Refactor replay based scripts by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/173

Documentation changes

A significant amount of documentation changes (tracked by https://github.com/vwxyzjn/cleanrl/issues/121).

See the overview documentation page here: https://docs.cleanrl.dev/rl-algorithms/overview/

Add ddpg_continuous_action.py docs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/137

Fix DDPG docs' description by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/139

Fix typo in DDPG docs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/140

Fix incorrect links in the DDPG docs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/142

DDPG documnetation tweaks; added Q loss equations and light explanation by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/145

Add dqn_atari.py documentation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/124

Add documentation for td3_continuous_action.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/141

SAC Documentation - Benchmarks - Minor code tweaks by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/146

Add docs for c51.py and c51_atari.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/159

Add docs for dqn.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/157

Address stale documentation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/169

Documentation improvement - fix links and mkdocs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/181

Improve documentation and contribution guide by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/189

Fix documentation links in README.md by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/192

Fix the implemented varaints section in PPO by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/193

Misclanouse changes

Add Pull Request template by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/122

Amend license to give proper attribution by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/152

Introduce better contribution guide by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/154

Fix the default wandb project name in ppo_atari_envpool.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/160

Removes unmaintained scripts by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/170

Add PPO documentation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/163

Add docs header by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/174

Update README.md by @ElliotMunro200 in https://github.com/vwxyzjn/cleanrl/pull/177

Update issue_template.md by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/180

Temporarily Remove PPO-RND by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/190

Utility changes

Export requirements.txt automatically by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/143

Auto-upgrade syntax via pyupgrade by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/158

Introduce benchmark utilities by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/165

New Contributors

@ElliotMunro200 made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/177

@Dipamc77 made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/186

Full Changelog: https://github.com/vwxyzjn/cleanrl/compare/v0.6.0...v1.0.0b1
Source code(tar.gz)
Source code(zip)
v0.6.0(Mar 16, 2022)
What's Changed

Update paper citation entry by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/91

Clean up stale files by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/95

Refactor formats in parse_args by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/78

Add Gitpod support by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/94

Reorganize README.md by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/93

Downgrade setuptools by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/98

Fix readme links by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/104

Refactor value based methods by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/102

Introduce pre-commit pipelines by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/107

Refactor PPG and PPO for procgen by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/108

Update documentation on PPG and PPO Procgen by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/112

Add PPO Atari LSTM example by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/83

Prototype Envpool Support by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/100

Fix replay buffer compatibility with mujoco envs by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/113

Add the isort and black badges by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/119

Refactor parse_args() by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/118

Add ppo.py documentation by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/120

Replace episode_reward with episodic_return by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/125

Refactor ppo_pettingzoo.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/128

Update gym to 0.23.0 by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/129

Add SPS and q-values metrics for value-based methods by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/126

Make seed work again in value methods by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/134

Remove offline DQN scripts by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/135

Deprecate apex_dqn_atari.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/136

Update to gym==0.23.1 by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/138

Full Changelog: https://github.com/vwxyzjn/cleanrl/compare/v0.5.0...v0.6.0
Source code(tar.gz)
Source code(zip)
v0.5.0(Nov 12, 2021)
What's Changed

Use Poetry as the package manager by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/50

Remove links to deleted code on README algorithms by @FelipeMartins96 in https://github.com/vwxyzjn/cleanrl/pull/54

Add paper plotting utilities by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/55

Reorganization of files. by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/56

Bump Gym's version to 0.21.0 by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/61

Automatically Download Atari Roms by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/62

Make Spyder Editor Optional by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/66

Support Python 3.7.1+ by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/67

ddpg_continuous: Addded env argument to actor and target actor by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/69

Add pytest as an optional dependency by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/71

Remove SB3 dependency in ppo_continuous_action.py by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/72

Add e2e tests by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/70

Fix #74 SAC consistency in logging and training to match other scripts by @dosssman in https://github.com/vwxyzjn/cleanrl/pull/75

Add MuJoCo environments support. by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/76

Only run tests given changes to the cleanrl directory by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/77

Prototype Documentation Site by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/64

Cloud Utilities Improvement by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/65

Import built docker image to local registry by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/80

Remove docker dummy cache by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/81

Allow buildx to save to local and push by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/82

Rollback back PyTorch version for better compatibility by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/84

Cloud utilities refactor by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/85

Prepare for 0.5.0 release by @vwxyzjn in https://github.com/vwxyzjn/cleanrl/pull/88

New Contributors

@FelipeMartins96 made their first contribution in https://github.com/vwxyzjn/cleanrl/pull/54

Full Changelog: https://github.com/vwxyzjn/cleanrl/compare/v0.4.8...v0.5.0
Source code(tar.gz)
Source code(zip)
v0.4.8(May 16, 2021)

Source code(tar.gz)
Source code(zip)
v0.4.7(May 12, 2021)

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Source code(zip)
v0.4.6(May 12, 2021)

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v0.4.5(Apr 19, 2021)

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v0.4.4(Apr 16, 2021)

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Source code(zip)
v0.2.1(Apr 13, 2021)

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v0.4.3(Apr 11, 2021)

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v0.4.2(Apr 11, 2021)

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v0.4.1(Apr 11, 2021)

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Source code(zip)
0.4.0(Sep 24, 2020)
What's new in the 0.4.0 release

Added contribution guide here https://github.com/vwxyzjn/cleanrl/blob/master/CONTRIBUTING.md. We welcome contribution of new algorithms and new games to be added to the Open RL Benchmark (http://benchmark.cleanrl.dev/)

Added tables for the benchmark results with standard deviations created by (https://github.com/vwxyzjn/cleanrl/blob/master/benchmark/plots.py)

Atari Results

| gym_id | apex_dqn_atari_visual | c51_atari_visual | dqn_atari_visual | ppo_atari_visual | |:----------------------------|:------------------------|:-------------------|:-------------------|:-------------------| | BeamRiderNoFrameskip-v4 | 2936.93 ± 362.18 | 13380.67 ± 0.00 | 7139.11 ± 479.11 | 2053.08 ± 83.37 | | QbertNoFrameskip-v4 | 3565.00 ± 690.00 | 16286.11 ± 0.00 | 11586.11 ± 0.00 | 17919.44 ± 383.33 | | SpaceInvadersNoFrameskip-v4 | 1019.17 ± 356.94 | 1099.72 ± 14.72 | 935.40 ± 93.17 | 1089.44 ± 67.22 | | PongNoFrameskip-v4 | 19.06 ± 0.83 | 18.00 ± 0.00 | 19.78 ± 0.22 | 20.72 ± 0.28 | | BreakoutNoFrameskip-v4 | 364.97 ± 58.36 | 386.10 ± 21.77 | 353.39 ± 30.61 | 380.67 ± 35.29 |

Mujoco Results

| gym_id | ddpg_continuous_action | td3_continuous_action | ppo_continuous_action | |:--------------------|:-------------------------|:------------------------|:------------------------| | Reacher-v2 | -6.25 ± 0.54 | -6.65 ± 0.04 | -7.86 ± 1.47 | | Pusher-v2 | -44.84 ± 5.54 | -59.69 ± 3.84 | -44.10 ± 6.49 | | Thrower-v2 | -137.18 ± 47.98 | -80.75 ± 12.92 | -58.76 ± 1.42 | | Striker-v2 | -193.43 ± 27.22 | -269.63 ± 22.14 | -112.03 ± 9.43 | | InvertedPendulum-v2 | 1000.00 ± 0.00 | 443.33 ± 249.78 | 968.33 ± 31.67 | | HalfCheetah-v2 | 10386.46 ± 265.09 | 9265.25 ± 1290.73 | 1717.42 ± 20.25 | | Hopper-v2 | 1128.75 ± 9.61 | 3095.89 ± 590.92 | 2276.30 ± 418.94 | | Swimmer-v2 | 114.93 ± 29.09 | 103.89 ± 30.72 | 111.74 ± 7.06 | | Walker2d-v2 | 1946.23 ± 223.65 | 3059.69 ± 1014.05 | 3142.06 ± 1041.17 | | Ant-v2 | 243.25 ± 129.70 | 5586.91 ± 476.27 | 2785.98 ± 1265.03 | | Humanoid-v2 | 877.90 ± 3.46 | 6342.99 ± 247.26 | 786.83 ± 95.66 |

Pybullet Results

| gym_id | ddpg_continuous_action | td3_continuous_action | ppo_continuous_action | |:-----------------------------------|:-------------------------|:------------------------|:------------------------| | MinitaurBulletEnv-v0 | -0.17 ± 0.02 | 7.73 ± 5.13 | 23.20 ± 2.23 | | MinitaurBulletDuckEnv-v0 | -0.31 ± 0.03 | 0.88 ± 0.34 | 11.09 ± 1.50 | | InvertedPendulumBulletEnv-v0 | 742.22 ± 47.33 | 1000.00 ± 0.00 | 1000.00 ± 0.00 | | InvertedDoublePendulumBulletEnv-v0 | 5847.31 ± 843.53 | 5085.57 ± 4272.17 | 6970.72 ± 2386.46 | | Walker2DBulletEnv-v0 | 567.61 ± 15.01 | 2177.57 ± 65.49 | 1377.68 ± 51.96 | | HalfCheetahBulletEnv-v0 | 2847.63 ± 212.31 | 2537.34 ± 347.20 | 2347.64 ± 51.56 | | AntBulletEnv-v0 | 2094.62 ± 952.21 | 3253.93 ± 106.96 | 1775.50 ± 50.19 | | HopperBulletEnv-v0 | 1262.70 ± 424.95 | 2271.89 ± 24.26 | 2311.20 ± 45.28 | | HumanoidBulletEnv-v0 | -54.45 ± 13.99 | 937.37 ± 161.05 | 204.47 ± 1.00 | | BipedalWalker-v3 | 66.01 ± 127.82 | 78.91 ± 232.51 | 272.08 ± 10.29 | | LunarLanderContinuous-v2 | 162.96 ± 65.60 | 281.88 ± 0.91 | 215.27 ± 10.17 | | Pendulum-v0 | -238.65 ± 14.13 | -345.29 ± 47.40 | -1255.62 ± 28.37 | | MountainCarContinuous-v0 | -1.01 ± 0.01 | -1.12 ± 0.12 | 93.89 ± 0.06 |

Other Results

| gym_id | ppo | dqn | |:---------------|:---------------|:----------------| | CartPole-v1 | 500.00 ± 0.00 | 182.93 ± 47.82 | | Acrobot-v1 | -80.10 ± 6.77 | -81.50 ± 4.72 | | MountainCar-v0 | -200.00 ± 0.00 | -142.56 ± 15.89 | | LunarLander-v2 | 46.18 ± 53.04 | 144.52 ± 1.75 |

Added experimental support for Apex-DQN that is significantly faster than DQN. See https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/apex_dqn_atari_visual.py. In the game of breakout, Apex-DQN takes less than 4 hours to achieve around 360 episode reward. In contrast, it took 25 hours for DQN to reach 360 episode rewards.

Our implementation is a little different from the original. First, in pytorch's ecosystem there isn't a well-mainained distributed prioritized experience buffer such as https://github.com/deepmind/reverb. So instead we split a single prioritized replay buffer of size 100000 to two prioritized replay of size 50000 in different data-processors in sub-processes to prepare data for the worker. This is kind of a work around and a hack but according to our benchmark, it works empirically good and fast enough.

Benchmarked Learning Curves | Atari :-------------------------:|:-------------------------: Metrics, logs, and recorded videos are at | cleanrl.benchmark/reports/Atari | | |

Supported CarRacing-v0 by PPO in the Experimental Domains. It is our first example with pixel observation space and continuous action space. See https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/experiments/ppo_car_racing.py.

During our experiments, we found the normalization of observation and reward seems to have a huge impact on PPO's performance, probably due to the large range of rewards provided by CarRacing-v0 (e.g. if dies you get -100 reward, but PPO is anecdotally sensitive to this kind of large rewards).

Source code(tar.gz)
Source code(zip)
0.3.0(Aug 1, 2020)

See https://streamable.com/cq8e62 for a demo

Significant amount of effort was put into the making of Open RL Benchmark (http://benchmark.cleanrl.dev/). It provides benchmark of popular Deep Reinforcement Learning algorithms in 34+ games with unprecedented level of transparency, openness, and reproducibility.

In addition, the legacy common.py is depreciated in favor of using single-file implementations.
Source code(tar.gz)
Source code(zip)
0.2.1(Jan 9, 2020)

We've made the SAC algorithm works for both continuous and discrete action spaces, with primary references from the following papers:

https://arxiv.org/abs/1801.01290 https://arxiv.org/abs/1812.05905 https://arxiv.org/abs/1910.07207

My personal thanks to everyone who participated in the monthly dev cycle and, in particular, @dosssman who implemented the SAC with discrete action spaces.

Additional improvement include support gym.wrappers.Monitor to automatically record agent’s performance at certain episodes (default is 1, 2, 9, 28, 65, ... 1000, 2000, 3000) and integrate with wandb. (so cool, see screenshot below) #4 Use the same replay buffer from minimalRL for DQN and SAC #5

https://app.wandb.ai/cleanrl/cleanrl.benchmark

Source code(tar.gz)
Source code(zip)
V0.1(Oct 7, 2019)

This is the initial release 🙌🙌

Working on more algorithms and where and bug fixes for the 1.0 release :) Comments and PR are more than welcome.
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Source code(zip)

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features

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