[Enduro] The agent is now a professional Enduro-player. See `enduro.g…

…if` or `example.ipynb` of the agent in action!

README.txt

@@ -35,32 +35,4 @@ enduro
   * input space     [210, 160, 1]
   * action space    [9,]
 
-The implementation, examples and results are presented in their corresponding directories. During
-training of the latter four games, Orion HPC (https://orion.nmbu.no) at the Norwegian University of
-Life Sciences (NMBU) provided computational resources.
-
----
-
-N.b., in order for the examples to access atari games from `gymnasium`, Python<=3.10 must be used.
-
----
-
-Relevant papers:
-
-- "Human-level control through deep reinforcement learning"
-                                    doi:10.1038/nature14236
-- "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm"
-                                                                        arXiv:1712.01815v1
-
----
-
-Learning goals:
-
-- Understand and know how to build, use and deploy reinforcement learning algorithms
-  * Experiment with reinforcement agent(s) (for instance playing video-games)
-
-Learning outcomes:
-
-- Be competent in modern deep learning situations
-  * Understand (and to some extent be able to reproduce) cutting-edge “artificial intelligence”
-    models
+The implementation, examples and results are pr