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* Update projects.rst Added gym-pybullet-drones * Update projects.rst Longer title underline * Update changelog Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>
63 lines
2.4 KiB
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63 lines
2.4 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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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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-----------------------------------------------------------------------------------
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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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Reacher
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-------
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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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-------
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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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-------------------
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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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