deep-reinforcement-learning-book/Chapter16-Robot-Learning-in-Simulation

Chapter 16 Robot Learning in Simulation in book Deep Reinforcement Learning: example of Sawyer robot learning to reach the target with paralleled Soft Actor-Critic (SAC) algorithm, using PyRep for Sawyer robot simulation and game building. The environment is wrapped into OpenAI Gym format.

Chapter 16: Robot Learning in Simulation (Project 4)

Description:

Example of Sawyer robot learning to reach the target with paralleled Soft Actor-Critic (SAC) algorithm, using PyRep for Sawyer robot simulation and game building. The environment is wrapped into OpenAI Gym format.

Dependencies:

• V-REP 3.6.2
• PyRep
• PyTorch

Note:

• The later version of V-REP 3.6.2 is renamed CoppeliaSim after verison 4.0.0, which may have some incompatible issues with PyRep during the process of this project, so we suggest to use V-REP 3.6.2 here and the maintained PyRep in our repository.
• The official repository of PyRep is here, but we maintain a stable version here in our repository for supporting V-REP 3.6.2, please use the version we provide (here) for avoiding unnecessary incompatibility.

Contents:

• arms/: object models of arms;
• hands/: object models of grippers;
• objects/: models of other objects in the scene;
• scenes/: built scenes for Sawyer robot grasping;
• figures/: figures for displaying;
• model/: the model after training, and two pre-trained models with different reward functions;
• data/: reward logs of with different reward functions;
• sawyer_grasp_env_boundingbox.py: script of Sawyer robot grasping environment;
• sac_learn.py: pralleled Soft Actor-Critic algorithm for solving Sawyer robot grasping task;
• reward_log.npy: log of episode reward during training;
• plot.ipynb: displaying the learning curves.

Usage:

0. First check the environment can run successfully:

   $ python sawyer_grasp_env_boundingbox.py

   If it works properly with VRep called to run a scene, with Sawyer robot arm moving randomly, then go to next step; otherwise check the dependencies for necessary packages and versions.

1. Run $ python sac_learn.py --train for training the policy

2. Run $ python sac_learn.py --test for testing the trained policy, remember to change the trained_model_path, which is default to be the trained model we provided.

3. The training process will provide a reward_log.npy file for recording the reward value during training, which can be displayed with $ jupyter notebook in a new terminal, choose plot.ipynband Shift+Enter to run the first cell, shown as follows:

Authors:

Zihan Ding, Yanhua Huang

Citing:

@misc{DeepReinforcementLearning-Chapter16-RobotLearninginSimulation,
  author = {Zihan Ding, Yanhua Huang},
  title = {Chapter16-RobotLearninginSimulation},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/deep-reinforcement-learning-book/Chapter16-Robot-Learning-in-Simulation}},
}

or

@book{deepRL-2020,
 title={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
 editor={Hao Dong, Zihan Ding, Shanghang Zhang},
 author={Hao Dong, Zihan Ding, Shanghang Zhang, Hang Yuan, Hongming Zhang, Jingqing Zhang, Yanhua Huang, Tianyang Yu, Huaqing Zhang, Ruitong Huang},
 publisher={Springer Nature},
 note={\url{http://www.deepreinforcementlearningbook.org}},
 year={2020}
}