Udacity Reacher Project with Unity (#79)

* Add the reacher project to the sample projects

* Update the change log

* Remove github incompatible link notation

* Update changelog.rst

Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>

docs/misc/changelog.rst

@@ -37,6 +37,7 @@ Documentation:
 ^^^^^^^^^^^^^^
 - Updated notebook links
 - Fixed a typo in the section of Enjoy a Trained Agent, in RL Baselines3 Zoo README. (@blurLake)
+- Added Unity reacher to the projects page (@koulakis)
 
 
 
@@ -339,4 +340,4 @@ And all the contributors:
 @Miffyli @dwiel @miguelrass @qxcv @jaberkow @eavelardev @ruifeng96150 @pedrohbtp @srivatsankrishnan @evilsocket
 @MarvineGothic @jdossgollin @SyllogismRXS @rusu24edward @jbulow @Antymon @seheevic @justinkterry @edbeeching
 @flodorner @KuKuXia @NeoExtended @PartiallyTyped @mmcenta @richardwu @kinalmehta @rolandgvc @tkelestemur @mloo3
-@tirafesi @blurLake
+@tirafesi @blurLake @koulakis 

docs/misc/projects.rst

@@ -25,3 +25,15 @@ It was the starting point of Stable-Baselines3.
 | Author: Antonin Raffin, Freek Stulp
 | Github: https://github.com/DLR-RM/stable-baselines3/tree/sde
 | Paper: https://arxiv.org/abs/2005.05719
+
+Reacher
+-------
+A solution to the second project of the Udacity deep reinforcement learning course.
+It is an example of:
+
+- wrapping single and multi-agent Unity environments to make them usable in Stable-Baselines3
+- 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)
+- generating several pre-trained models which solve the reacher environment
+
+| Author: Marios Koulakis
+| Github: https://github.com/koulakis/reacher-deep-reinforcement-learning