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Add gym-pybullet-drones project (#358)
* Update projects.rst Added gym-pybullet-drones * Update projects.rst Longer title underline * Update changelog Co-authored-by: Antonin Raffin <antonin.raffin@ensta.org>
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@ -3,8 +3,32 @@
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Changelog
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==========
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Release 1.1.0a0 (WIP)
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---------------------------
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Breaking Changes:
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^^^^^^^^^^^^^^^^^
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New Features:
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^^^^^^^^^^^^^
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Bug Fixes:
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^^^^^^^^^^
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Deprecations:
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^^^^^^^^^^^^^
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Others:
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^^^^^^^
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Documentation:
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^^^^^^^^^^^^^^
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- Added gym pybullet drones project (@JacopoPan)
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Release 1.0 (2021-03-15)
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-------------------------------
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------------------------
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**First Major Version**
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@ -610,4 +634,4 @@ And all the contributors:
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@tirafesi @blurLake @koulakis @joeljosephjin @shwang @rk37 @andyshih12 @RaphaelWag @xicocaio
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@diditforlulz273 @liorcohen5 @ManifoldFR @mloo3 @SwamyDev @wmmc88 @megan-klaiber @thisray
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@tfederico @hn2 @LucasAlegre @AptX395 @zampanteymedio @decodyng @ardabbour @lorenz-h @mschweizer @lorepieri8
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@ShangqunYu @PierreExeter
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@ShangqunYu @PierreExeter @JacopoPan
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@ -7,15 +7,7 @@ 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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..
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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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rl_reach
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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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@ -56,4 +48,16 @@ A simple interface to instantiate RL environments with SUMO for Traffic Signal C
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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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| 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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@ -1 +1 @@
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1.0
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1.1.0a0
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