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* Add SUMO-RL as example project in the docs * Fixed docstring of AtariWrapper which was not inside of __init__ * Updated changelog regarding docs * Fix docstring of classes in atari_wrappers.py which were inside the constructor * Formated docstring with black Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
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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 Racing Robot
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.. --------------------------
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.. Implementation of reinforcement learning approach to make a donkey car learn to race.
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.. Uses SAC on autoencoder features
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.. | Author: Antonin Raffin (@araffin)
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.. | Github repo: https://github.com/araffin/RL-Racing-Robot
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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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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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-------
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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 |