Update README.md

README.md

@@ -4,6 +4,10 @@
 ## Introduction
 The goal of this project is to develop an AI Bot to learn and play the popular game Snake from scratch. The implementation includes playing by human player, using rule-based, Q-learn, and finally Deep Reinforcement Learning algorithms. For Q-learning and Deep Reinforcement Learning, no rules about the game are given, and initially the Bot has to try exploring all options to learn what to do to get a good reward.
 
+The code follows the following tutorial:
+https://towardsdatascience.com/how-to-teach-an-ai-to-play-games-deep-reinforcement-learning-28f9b920440a
+
+
 ## Install
 This project requires Python 3.8 with the pygame library installed, as well as Keras with Tensorflow backend.
 ```bash
@@ -25,13 +29,13 @@ python main.py --help
 
 To change to a Bot, modify main.py by uncommenting the appropriate algorithm to run:
 ```
-    ## AI selector, pick one:
-    algo = AI_Player0() # do nothing, let human player control
-    #algo = AI_RuleBased() # rule-based algorithm
-    #algo = AI_RLQ()       # Q-learning - training mode
-    #algo = AI_RLQ(False)  # Q-learning - testing mode, no exploration
-    #algo = AI_DQN()       # DQN - training mode
-    #algo = AI_DQN(False)  # DQN - testing mode, no exploration
+## AI selector, pick one:
+algo = AI_Player0() # do nothing, let human player control
+#algo = AI_RuleBased() # rule-based algorithm
+#algo = AI_RLQ()       # Q-learning - training mode
+#algo = AI_RLQ(False)  # Q-learning - testing mode, no exploration
+#algo = AI_DQN()       # DQN - training mode
+#algo = AI_DQN(False)  # DQN - testing mode, no exploration
 ```
 
 ## Trained Data (for Q-Learning)