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* add furuta pendulum project * Update changelog to reflect addition to docs Co-authored-by: Anssi <kaneran21@hotmail.com>
163 lines
7.7 KiB
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163 lines
7.7 KiB
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.. _projects:
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Projects
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=========
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This is a list of projects using stable-baselines3.
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Please tell us, if you want your project to appear on this page ;)
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DriverGym
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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.
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| Authors: Parth Kothari, Christian Perone, Luca Bergamini, Alexandre Alahi, Peter Ondruska
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| Github: https://github.com/lyft/l5kit
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| Paper: https://arxiv.org/abs/2111.06889
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RL Reach
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--------
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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.
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| Authors: Pierre Aumjaud, David McAuliffe, Francisco Javier Rodríguez Lera, Philip Cardiff
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| Github: https://github.com/PierreExeter/rl_reach
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| Paper: https://arxiv.org/abs/2102.04916
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Generalized State Dependent Exploration for Deep Reinforcement Learning in Robotics
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An exploration method to train RL agent directly on real robots.
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It was the starting point of Stable-Baselines3.
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| Author: Antonin Raffin, Freek Stulp
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| Github: https://github.com/DLR-RM/stable-baselines3/tree/sde
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| Paper: https://arxiv.org/abs/2005.05719
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Furuta Pendulum Robot
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Everything you need to build and train a rotary inverted pendulum, also know as a furuta pendulum! This makes use of gSDE listed above.
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The Github repository contains code, CAD files and a bill of materials for you to build the robot. You can watch `a video overview of the project here <https://www.youtube.com/watch?v=Y6FVBbqjR40>`_.
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| Authors: Armand du Parc Locmaria, Pierre Fabre
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| Github: https://github.com/Armandpl/furuta
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Reacher
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A solution to the second project of the Udacity deep reinforcement learning course.
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It is an example of:
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- wrapping single and multi-agent Unity environments to make them usable in Stable-Baselines3
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- 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)
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- generating several pre-trained models which solve the reacher environment
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| Author: Marios Koulakis
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| Github: https://github.com/koulakis/reacher-deep-reinforcement-learning
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SUMO-RL
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A simple interface to instantiate RL environments with SUMO for Traffic Signal Control.
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- Supports Multiagent RL
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- Compatibility with gym.Env and popular RL libraries such as stable-baselines3 and RLlib
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- Easy customisation: state and reward definitions are easily modifiable
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| Author: Lucas Alegre
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| Github: https://github.com/LucasAlegre/sumo-rl
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gym-pybullet-drones
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PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control.
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- Physics-based simulation for the development and test of quadcopter control.
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- Compatibility with ``gym.Env``, RLlib's MultiAgentEnv.
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- Learning and testing script templates for stable-baselines3 and RLlib.
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| Author: Jacopo Panerati
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| Github: https://github.com/utiasDSL/gym-pybullet-drones/
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| Paper: https://arxiv.org/abs/2103.02142
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SuperSuit
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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:
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-Wrappers that apply lambda functions to observations, actions, or rewards with a single line of code.
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-All wrappers can be used natively on vector environments, wrappers exist to Gym environments to vectorized environments and concatenate multiple vector environments together
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-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:
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| Author: Justin Terry
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| GitHub: https://github.com/PettingZoo-Team/SuperSuit
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| Tutorial on multi-agent support in stable baselines: https://towardsdatascience.com/multi-agent-deep-reinforcement-learning-in-15-lines-of-code-using-pettingzoo-e0b963c0820b
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Rocket League Gym
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A fully custom python API and C++ DLL to treat the popular game Rocket League like an OpenAI Gym environment.
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- Dramatically increases the rate at which the game runs.
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- Supports full configuration of initial states, observations, rewards, and terminal states.
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- Supports multiple simultaneous game clients.
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- Supports multi-agent training and self-play.
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- Provides custom wrappers for easy use with stable-baselines3.
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| Authors: Lucas Emery, Matthew Allen
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| GitHub: https://github.com/lucas-emery/rocket-league-gym
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| Website: https://rlgym.github.io
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gym-electric-motor
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An OpenAI gym environment for the simulation and control of electric drive trains.
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Think of Matlab/Simulink for electric motors, inverters, and load profiles, but non-graphical and open-source in Python.
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`gym-electric-motor` offers a rich interface for customization, including
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- plug-and-play of different control algorithms ranging from classical controllers (like field-oriented control) up to any RL agent you can find,
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- reward shaping,
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- load profiling,
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- finite-set or continuous-set control,
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- one-phase and three-phase motors such as induction machines and permanent magnet synchronous motors, among others.
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SB3 is used as an example in one of many tutorials showcasing the easy usage of `gym-electric-motor`.
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| Author: `Paderborn University, LEA department <https://github.com/upb-lea>`_
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| GitHub: https://github.com/upb-lea/gym-electric-motor
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| SB3 Tutorial: `Colab Link <https://colab.research.google.com/github/upb-lea/gym-electric-motor/blob/master/examples/reinforcement_learning_controllers/stable_baselines3_dqn_disc_pmsm_example.ipynb>`_
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| Paper: `JOSS <https://joss.theoj.org/papers/10.21105/joss.02498>`_, `TNNLS <https://ieeexplore.ieee.org/document/9241851>`_, `ArXiv <https://arxiv.org/abs/1910.09434>`_
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policy-distillation-baselines
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A PyTorch implementation of Policy Distillation for control, which has well-trained teachers via Stable Baselines3.
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- `policy-distillation-baselines` provides some good examples for policy distillation in various environment and using reliable algorithms.
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- All well-trained models and algorithms are compatible with Stable Baselines3.
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| Authors: Junyeob Baek
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| GitHub: https://github.com/CUN-bjy/policy-distillation-baselines
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| Demo: `link <https://github.com/CUN-bjy/policy-distillation-baselines/issues/3#issuecomment-817730173>`_
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highway-env
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A minimalist environment for decision-making in Autonomous Driving.
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Driving policies can be trained in different scenarios, and several notebooks using SB3 are provided as examples.
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| Author: `Edouard Leurent <https://edouardleurent.com>`_
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| GitHub: https://github.com/eleurent/highway-env
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| Examples: `Colab Links <https://github.com/eleurent/highway-env/tree/master/scripts#using-stable-baselines3>`_
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tactile-gym
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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.
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| Author: Alex Church
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| GitHub: https://github.com/ac-93/tactile_gym
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| Paper: https://arxiv.org/abs/2106.08796
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| Website: `tactile-gym website <https://sites.google.com/my.bristol.ac.uk/tactile-gym-sim2real/home>`_
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