.. _projects: Projects ========= This is a list of projects using stable-baselines3. Please tell us, if you want your project to appear on this page ;) DriverGym --------- 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. | Authors: Parth Kothari, Christian Perone, Luca Bergamini, Alexandre Alahi, Peter Ondruska | Github: https://github.com/lyft/l5kit | Paper: https://arxiv.org/abs/2111.06889 RL Reach -------- 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. | Authors: Pierre Aumjaud, David McAuliffe, Francisco Javier Rodríguez Lera, Philip Cardiff | Github: https://github.com/PierreExeter/rl_reach | Paper: https://arxiv.org/abs/2102.04916 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. | Author: Antonin Raffin, Freek Stulp | Github: https://github.com/DLR-RM/stable-baselines3/tree/sde | Paper: https://arxiv.org/abs/2005.05719 Reacher ------- 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 | Author: Marios Koulakis | Github: https://github.com/koulakis/reacher-deep-reinforcement-learning SUMO-RL ------- 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 | Author: Lucas Alegre | Github: https://github.com/LucasAlegre/sumo-rl gym-pybullet-drones ------------------- PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control. - Physics-based simulation for the development and test of quadcopter control. - Compatibility with ``gym.Env``, RLlib's MultiAgentEnv. - Learning and testing script templates for stable-baselines3 and RLlib. | Author: Jacopo Panerati | Github: https://github.com/utiasDSL/gym-pybullet-drones/ | Paper: https://arxiv.org/abs/2103.02142 SuperSuit --------- 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: | Author: Justin Terry | GitHub: https://github.com/PettingZoo-Team/SuperSuit | Tutorial on multi-agent support in stable baselines: https://towardsdatascience.com/multi-agent-deep-reinforcement-learning-in-15-lines-of-code-using-pettingzoo-e0b963c0820b 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. | Authors: Lucas Emery, Matthew Allen | GitHub: https://github.com/lucas-emery/rocket-league-gym | Website: https://rlgym.github.io gym-electric-motor ------------------- 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`. | Author: `Paderborn University, LEA department `_ | GitHub: https://github.com/upb-lea/gym-electric-motor | SB3 Tutorial: `Colab Link `_ | Paper: `JOSS `_, `TNNLS `_, `ArXiv `_ policy-distillation-baselines ------------------------------ 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. | Authors: Junyeob Baek | GitHub: https://github.com/CUN-bjy/policy-distillation-baselines | Demo: `link `_ highway-env ------------------- 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. | Author: `Edouard Leurent `_ | GitHub: https://github.com/eleurent/highway-env | Examples: `Colab Links `_ tactile-gym ------------------- 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. | Author: Alex Church | GitHub: https://github.com/ac-93/tactile_gym | Paper: https://arxiv.org/abs/2106.08796 | Website: `tactile-gym website `_