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--- learning:intro [2019/12/03 15:46] – orel
+++ learning:intro [2024/03/18 15:06] (current) – external edit 127.0.0.1
@@ Line 10: / Line 10: @@
   * http://ai.berkeley.edu/lecture_slides.html
+  * https://github.com/janishar/mit-deep-learning-book-pdf (MIT Book)
+  * https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf (RL)
 === Intro à TensorFlow ===
@@ Line 27: / Line 29: @@
   * Gym (game simulator) : https://gym.openai.com/ (pip3 install gym atari_py)
   * [[https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb|quick start colab]]
-  * Q Learning avec Keras+Gym : https://keon.io/deep-q-learning/
+  * Deep Q Learning (DQN) avec Keras+Gym : Cart Pole Sample
+    * https://keon.io/deep-q-learning/
+    * https://medium.com/@gtnjuvin/my-journey-into-deep-q-learning-with-keras-and-gym-3e779cc12762
+=== Atari 2600 ===
+  * Playing Atari with Deep Reinforcement Learning, Deepmind.com (2013).
+    * https://arxiv.org/pdf/1312.5602.pdf
+  * Learning from the memory of Atari 2600 (2016).
+    * https://deepsense.ai/playing-atari-on-ram-with-deep-q-learning/
+    * https://github.com/sygi/deep_q_rl
+    * https://cdn-sv1.deepsense.ai/wp-content/uploads/2016/09/1605.01335v1-4.pdf
+  * DQN : Atari Breakout examples
+    * https://becominghuman.ai/lets-build-an-atari-ai-part-1-dqn-df57e8ff3b26
+    * https://github.com/rbrigden/breakout-ram-dqn
+    * https://towardsdatascience.com/tutorial-double-deep-q-learning-with-dueling-network-architectures-4c1b3fb7f756
+    * https://github.com/andi611/DQN-Deep-Q-Network-Atari-Breakout-Tensorflow
+    * https://deepsense.ai/playing-atari-on-ram-with-deep-q-learning/
+    * ...
   * Reinforcement Learning + Keras + Atari : https://github.com/rlcode/reinforcement-learning
+  * DDQN + Genetic
+    * https://towardsdatascience.com/atari-reinforcement-learning-in-depth-part-1-ddqn-ceaa762a546f
+    * https://towardsdatascience.com/atari-solving-games-with-ai-part-2-neuroevolution-aac2ebb6c72b
-=== Graph ===
+=== About Graph ===
   * Graph Representation Learning: https://www.youtube.com/watch?v=YrhBZUtgG4E
+  * GCN : https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780
+  * https://rlgm.github.io/papers/41.pdf
+  * Semi-Supervised Classification with Graph Convolutional Networks: https://arxiv.org/abs/1609.02907
 === Fun! ===
-  * https://github.com/greerviau/SnakeAI
+  * https://github.com/greerviau/SnakeAI + https://www.youtube.com/watch?v=zIkBYwdkuTk
   * Alpha Toe
     * https://github.com/DanielSlater/AlphaToe