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.

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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)

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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: