S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) Toolbox for Robotics
Github repository: https://github.com/araffin/robotics-rl-srl
Video: https://youtu.be/qNsHMkIsqJc
This repository was made to evaluate State Representation Learning methods using Reinforcement Learning. It integrates (automatic logging, plotting, saving, loading of trained agent) various RL algorithms (PPO, A2C, ARS, ACKTR, DDPG, DQN, ACER, CMA-ES, SAC, TRPO) along with different SRL methods (see SRL Repo) in an efficient way (1 Million steps in 1 Hour with 8-core cpu and 1 Titan X GPU).
We also release customizable Gym environments for working with simulation (Kuka arm, Mobile Robot in PyBullet, running at 250 FPS on a 8-core machine) and real robots (Baxter Robot, Robobo with ROS).