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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Jacopo Panerati 2021-03-19 19:50:43 -04:00 committed by GitHub
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Changelog
==========
Release 1.1.0a0 (WIP)
---------------------------
Breaking Changes:
^^^^^^^^^^^^^^^^^
New Features:
^^^^^^^^^^^^^
Bug Fixes:
^^^^^^^^^^
Deprecations:
^^^^^^^^^^^^^
Others:
^^^^^^^
Documentation:
^^^^^^^^^^^^^^
- Added gym pybullet drones project (@JacopoPan)
Release 1.0 (2021-03-15)
-------------------------------
------------------------
**First Major Version**
@ -610,4 +634,4 @@ And all the contributors:
@tirafesi @blurLake @koulakis @joeljosephjin @shwang @rk37 @andyshih12 @RaphaelWag @xicocaio
@diditforlulz273 @liorcohen5 @ManifoldFR @mloo3 @SwamyDev @wmmc88 @megan-klaiber @thisray
@tfederico @hn2 @LucasAlegre @AptX395 @zampanteymedio @decodyng @ardabbour @lorenz-h @mschweizer @lorepieri8
@ShangqunYu @PierreExeter
@ShangqunYu @PierreExeter @JacopoPan

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@ -7,15 +7,7 @@ This is a list of projects using stable-baselines3.
Please tell us, if you want your project to appear on this page ;)
.. RL Racing Robot
.. --------------------------
.. Implementation of reinforcement learning approach to make a donkey car learn to race.
.. Uses SAC on autoencoder features
..
.. | Author: Antonin Raffin (@araffin)
.. | Github repo: https://github.com/araffin/RL-Racing-Robot
rl_reach
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.
@ -56,4 +48,16 @@ A simple interface to instantiate RL environments with SUMO for Traffic Signal C
- Easy customisation: state and reward definitions are easily modifiable
| Author: Lucas Alegre
| Github: https://github.com/LucasAlegre/sumo-rl
| 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

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1.0
1.1.0a0