This is a list of projects using stable-baselines3. Please tell us, if you want your project to appear on this page ;)
An open-source Gym-compatible environment specifically tailored for developing RL algorithms for autonomous driving. DriverGym provides access to more than 1000 hours of expert logged data and also supports reactive and data-driven agent behavior. The performance of an RL policy can be easily validated using an extensive and flexible closed-loop evaluation protocol. We also provide behavior cloning baselines using supervised learning and RL, trained in DriverGym.
A platform for running reproducible reinforcement learning experiments for customisable robotic reaching tasks. This self-contained and straightforward toolbox allows its users to quickly investigate and identify optimal training configurations.
Generalized State Dependent Exploration for Deep Reinforcement Learning in Robotics¶
An exploration method to train RL agent directly on real robots. It was the starting point of Stable-Baselines3.
A solution to the second project of the Udacity deep reinforcement learning course. It is an example of:
wrapping single and multi-agent Unity environments to make them usable in Stable-Baselines3
creating experimentation scripts which train and run A2C, PPO, TD3 and SAC models (a better choice for this one is https://github.com/DLR-RM/rl-baselines3-zoo)
generating several pre-trained models which solve the reacher environment
A simple interface to instantiate RL environments with SUMO for Traffic Signal Control.
Supports Multiagent RL
Compatibility with gym.Env and popular RL libraries such as stable-baselines3 and RLlib
Easy customisation: state and reward definitions are easily modifiable
PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control.
Physics-based simulation for the development and test of quadcopter control.
gym.Env, RLlib’s MultiAgentEnv.
Learning and testing script templates for stable-baselines3 and RLlib.
SuperSuit contains easy to use wrappers for Gym (and multi-agent PettingZoo) environments to do all forms of common preprocessing (frame stacking, converting graphical observations to greyscale, max-and-skip for Atari, etc.). It also notably includes:
-Wrappers that apply lambda functions to observations, actions, or rewards with a single line of code. -All wrappers can be used natively on vector environments, wrappers exist to Gym environments to vectorized environments and concatenate multiple vector environments together -A wrapper is included that allows for using regular single agent RL libraries (e.g. stable baselines) to learn simple multi-agent PettingZoo environments, explained in this tutorial:
Rocket League Gym¶
A fully custom python API and C++ DLL to treat the popular game Rocket League like an OpenAI Gym environment.
Dramatically increases the rate at which the game runs.
Supports full configuration of initial states, observations, rewards, and terminal states.
Supports multiple simultaneous game clients.
Supports multi-agent training and self-play.
Provides custom wrappers for easy use with stable-baselines3.
An OpenAI gym environment for the simulation and control of electric drive trains. Think of Matlab/Simulink for electric motors, inverters, and load profiles, but non-graphical and open-source in Python.
gym-electric-motor offers a rich interface for customization, including - plug-and-play of different control algorithms ranging from classical controllers (like field-oriented control) up to any RL agent you can find, - reward shaping, - load profiling, - finite-set or continuous-set control, - one-phase and three-phase motors such as induction machines and permanent magnet synchronous motors, among others.
SB3 is used as an example in one of many tutorials showcasing the easy usage of gym-electric-motor.
A PyTorch implementation of Policy Distillation for control, which has well-trained teachers via Stable Baselines3.
policy-distillation-baselines provides some good examples for policy distillation in various environment and using reliable algorithms.
All well-trained models and algorithms are compatible with Stable Baselines3.
A minimalist environment for decision-making in Autonomous Driving.
Driving policies can be trained in different scenarios, and several notebooks using SB3 are provided as examples.
Suite of RL environments focussed on using a simulated tactile sensor as the primary source of observations. Sim-to-Real results across 4 out of 5 proposed envs.