hrxsd/rl_legged-wheeled-sim

RL_Legged-Wheeled-sim

Introduction

This project is a simulation of a two-legged-wheeled robot based on a deep reinforcement learning approach. The simulator used is MuJoco. The specific approach is to firstly encapsulate the robot as a standard reinforcement learning Python interface according to the gym style, including state feedback, action, reward function, reset, etc. The robot is then used to interact with the mujoco model through the Pytorch framework. Secondly, the SAC algorithm is built through Pytorch framework to interact with the MuJoco model to make the robot reach the goal point.

Environment Configuration

The project was done on Ubuntu20.04 with the following Python versions and library versions:

• Python 3.7.10
• MuJoco 210
• gym 0.21.0
• mujoco-py 2.1.2.14
• Pytorch 1.10.0
• numpy 1.21.6

Clarification

The project consists of three folders, a main file, a test file, and four other function files. These programs can be divided into three parts: the robot environment, the results, and the algorithms.

1. Environmental files

The robot simulation environment is mainly written in the envs folder, which consists of the asset folder, the file named biped.py, and the file named register.py.

biped.py encapsulates environment reset, step environment update (including reward function), status feedback, etc. The main function is to encapsulate the information of the robot inside the mujoco emulator through Python interface. register.py is the bootstrap file to guide the project to locate biped.py.

The asset folder mainly contains the mujoco simulator, Legged_wheel.xml, Legged_wheel.xml1, Legged_wheel.xml2, Legged_wheel.xml3 are four different simulation scenarios. meshes folder stores the robot parts in stl form.

2. Results file of the training

The trained neural network model is recorded in the models folder and called by test.py. The runtime data is recorded in the runs folder. To view the data, go to the runs folder and use the command:

tensorboard --logdir=[filename]

3. Reinforcement learning algorithm file

main.py is the main training program, the algorithm is SAC(sac.py) and the neural network model is written in Pytorch(model.py). Since SAC is offline reinforcement learning, it requires replay_memory(replay_memory.py). utils.py defines some script functions.

Usage

1. Install MuJoco 210
2. Install Anaconda
3. Create a conda environment
4. Install mujoco-py, gym and Pytorch
5. Download the project locally and run the following command for saving the running data:

cd RL_Legged-Wheeled-sim/legged_wheeled_mujoco
mkdir runs

6. Start a training session.

python main.py

7. Testing the trained model

python test.py