Reinforcement Learning Workspace

The basic workspace for reinforcement learning with CoppeliaSim (VREP) simulation environments, including some demonstrated project for beginners. Some tutorials can be found at:

• Zhihu: https://zhuanlan.zhihu.com/p/398874515
• WeChat: https://mp.weixin.qq.com/s/id7fw0eGBtLqEqr-zaKKPQ

All the demos have been tested on Ubuntu 20.04, with Anaconda python environment.

Environments setup

You have to install the following softwares and environments for this project, the recommend operating system is Ubuntu.

• CoppeliaSim 4.2 (https://www.coppeliarobotics.com/)
• Python 3.6+ (Anaconda is recommended)
• Gym (https://github.com/openai/gym)
• Stable-baselines3 (https://github.com/DLR-RM/stable-baselines3)
• Pytorch (https://pytorch.org/)
• Visdom (pip install visdom)

Demo 1: Cart-pole control with the A2C (modified SAC) algorithm

• Step 1: run CoppeliaSim, import cart_pole.ttt
• Step 2: run visdom in your terminal, open your browser, and visit link: localhost:8097
• Step 3: run the script named 'demo_cart_pole_learning.py' in the sub-path ./examples

Then we have: