SelfDrive_AI

Reinforcement learning for self-driving in a 3D simulation (Created using UNITY-3D)

1. Requirements for the SelfDrive_AI Gym

You need Python 3.6 or later to run the simulation. (Note: the current environment is only supported in windows) Also, you can directly interact with the simulation by clicking the exe file and then by using W,A, S and D keys.

Please follow the two links below to install Unity-Gym and Stable-Baselines. Also, you can train it using your custom reinforcement learning algorithms by following the OpenAI gym structure (https://gym.openai.com/).

Install Unity-Gym

Install Stable-Baselines3

mlagents can be installed using pip:

$ python3 -m pip install mlagents

The image below illustrates the target goal of the AIcar, where the car needs to explore all the trajectories to find the bridge first.

2. (Training) You can train the environment by using the code below which has OpenAI gym structure. It will save the training results into a log directory which you can view using tensorboard. Feel free to change the parameters inside the code

from stable_baselines3 import PPO, SAC, ppo
from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel
channel = EngineConfigurationChannel()
from gym_unity.envs import UnityToGymWrapper
from mlagents_envs.environment import UnityEnvironment
import time,os
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.policies import ActorCriticPolicy
import math


env_name = "./UnityEnv"
speed = 15


env = UnityEnvironment(env_name,seed=1, side_channels=[channel])
channel.set_configuration_parameters(time_scale =speed)
env= UnityToGymWrapper(env, uint8_visual=False) # OpenAI gym interface created using UNITY

time_int = int(time.time())

# Diretories for storing results 
log_dir = "stable_results/Euler_env_3{}/".format(time_int)
log_dirTF = "stable_results/tensorflow_log_Euler3{}/".format(time_int) 
os.makedirs(log_dir, exist_ok=True)

env = Monitor(env, log_dir, allow_early_resets=True)
env = DummyVecEnv([lambda: env])  # The algorithms require a vectorized environment to run


model = PPO(ActorCriticPolicy, env, verbose=1, tensorboard_log=log_dirTF, device='cuda')
model.learn(int(200000)) # you can change the step size
time_int2 = int(time.time()) 
print('TIME TAKEN for training',time_int-time_int2)
# # save the model
model.save("Env_model")


# # # # # LOAD FOR TESTING
# del model
model = PPO.load("Env_model")

obs = env.reset()

# Test the agent for 1000 steps after training

for i in range(400):
    action, states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    env.render()

To monitor the training progress using tensorboard you type the following command from the terminal

$ tensorboard --logdir "HERE PUT THE PATH TO THE DIRECTORY"

3. (Testing) The following code can be used to test the trained Humanoid Agent

from stable_baselines3 import PPO, SAC, ppo
from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel
channel = EngineConfigurationChannel()
from gym_unity.envs import UnityToGymWrapper
from mlagents_envs.environment import UnityEnvironment
import time,os
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.policies import ActorCriticPolicy
import math


env_name = "./UnityEnv"
speed = 1


env = UnityEnvironment(env_name,seed=1, side_channels=[channel])
channel.set_configuration_parameters(time_scale =speed)
env= UnityToGymWrapper(env, uint8_visual=False) # OpenAI gym interface created using UNITY

time_int = int(time.time())

# Diretories for storing results
log_dir = "stable_results/Euler_env_3{}/".format(time_int)
log_dirTF = "stable_results/tensorflow_log_Euler3{}/".format(time_int)
os.makedirs(log_dir, exist_ok=True)

env = Monitor(env, log_dir, allow_early_resets=True)
env = DummyVecEnv([lambda: env])  # The algorithms require a vectorized environment to run


model = PPO.load("Env_model")

obs = env.reset()

# Test the agent for 1000 steps after training

for i in range(1000):
    action, states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    env.render()

***Note: I am still developing the project by inducing more challenging constraints.

Reinforcement learning for self-driving in a 3D simulation

Related tags

Overview

SelfDrive_AI

1. Requirements for the SelfDrive_AI Gym

2. (Training) You can train the environment by using the code below which has OpenAI gym structure. It will save the training results into a log directory which you can view using tensorboard. Feel free to change the parameters inside the code

3. (Testing) The following code can be used to test the trained Humanoid Agent

Owner

Surajit Saikia

Machine learning and Deep learning models, deploy on telegram (the best social media)

Re-implementation of 'Grokking: Generalization beyond overfitting on small algorithmic datasets'

Repository for "Space-Time Correspondence as a Contrastive Random Walk" (NeurIPS 2020)

The code for replicating the experiments from the LFI in SSMs with Unknown Dynamics paper.

Code and data of the EMNLP 2021 paper "Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer"

Deep Learning: Architectures & Methods Project: Deep Learning for Audio Super-Resolution

DvD-TD3: Diversity via Determinants for TD3 version

U-Net Implementation: Convolutional Networks for Biomedical Image Segmentation" using the Carvana Image Masking Dataset in PyTorch

🏃‍♀️ A curated list about human motion capture, analysis and synthesis.

这是一个deeplabv3-plus-pytorch的源码，可以用于训练自己的模型。

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL"

The 1st place solution of track2 (Vehicle Re-Identification) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

The code repository for "PyCIL: A Python Toolbox for Class-Incremental Learning" in PyTorch.

ReLoss - Official implementation for paper "Relational Surrogate Loss Learning" ICLR 2022

Fake News Detection Using Machine Learning Methods

Circuit Training: An open-source framework for generating chip floor plans with distributed deep reinforcement learning

A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection

Unofficial Tensorflow-Keras implementation of Fastformer based on paper [Fastformer: Additive Attention Can Be All You Need](https://arxiv.org/abs/2108.09084).

Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch

SIMULEVAL A General Evaluation Toolkit for Simultaneous Translation