EL KYAL

ElKyal/FlappyAgent.py

@@ -0,0 +1,10 @@
+import numpy as np
+from keras.models import Sequential, load_model
+
+model = load_model("model.dqf")
+
+def FlappyPolicy(state, screen):
+    q = model.predict(np.array(list(state.values())).reshape(1,len(state)))
+
+
+    return(np.argmax(q)*119)

ElKyal/q_learn.py

@@ -0,0 +1,84 @@
+
+from ple.games.flappybird import FlappyBird
+from ple import PLE
+from keras import optimizers
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+import numpy as np
+import random
+
+
+model = Sequential()
+
+
+model.add(Dense(500, init='lecun_uniform', input_shape=(8,)))
+
+model.add(Activation('relu'))
+model.add(Dense(2, init='lecun_uniform'))
+model.add(Activation('linear'))
+model.compile(loss='mse', optimizer=optimizers.Adam(1e-4))
+
+
+epochs = 28000
+gamma = 0.99 # discount factor
+epsilon = 0.7 # epsilon-greddy
+batchSize = 256 # mini batch size
+
+jeu = FlappyBird()
+p= PLE(jeu, fps=30, frame_skip=1, num_steps=1,force_fps=True, display_screen=False)
+p.init()
+
+i=0
+
+for i in range(epochs):
+    p.reset_game()
+    state = jeu.getGameState()
+    state = np.array(list(state.values()))
+    while(not jeu.game_over()):
+
+
+
+        qval = model.predict(state.reshape(1,len(state)), batch_size=batchSize) #Learn Q (Q-learning) / model initialise avant (neural-network)
+        if (random.random() < epsilon): # exploration exploitation strategy    
+            action = np.random.randint(0,2)
+#             print(action)
+        else: #choose best action from Q(s,a) values
+            qval_av_action = [-9999]*2
+
+            for ac in range(0,2):
+                qval_av_action[ac] = qval[0][ac]
+            action = (np.argmax(qval_av_action))
+        #Take action, observe new state S'
+        #Observe reward
+        reward = p.act(119*action)
+        if reward == 1:
+            reaward = 1
+        elif reward == -5:
+            reward = -1000
+        new_state = jeu.getGameState()
+        new_state = np.array(list(new_state.values()))
+        # choose new reward values
+
+
+        #Get max_Q(S',a)
+        newQ = model.predict(new_state.reshape(1,len(state)), batch_size=batchSize)
+        maxQ = np.max(newQ)
+        y = np.zeros((1,2))
+        y[:] = qval[:]
+        if reward != -5: #non-terminal state
+            update = (reward + gamma * maxQ)
+        else:
+            update = reward
+        y[0][action] = update
+        print("Game #: %s" % (i,))
+        model.fit(state.reshape(1, len(state)), y, batch_size=batchSize, nb_epoch=3, verbose=0)
+        state = new_state
+
+
+    # update exploitation / exploration strategy
+    if epsilon > 0.2:
+        epsilon -= (1.0/epochs)
+
+
+
+model.save("model.dqf")

RandomBird/run.py → ElKyal/run.py

@@ -4,7 +4,7 @@
 import numpy as np
 from FlappyAgent import FlappyPolicy
 
-game = FlappyBird(graphics="fixed") # use "fancy" for full background, random bird color and random pipe color, use "fixed" (default) for black background and constant bird and pipe colors.
+game = FlappyBird() # use "fancy" for full background, random bird color and random pipe color, use "fixed" (default) for black background and constant bird and pipe colors.
 p = PLE(game, fps=30, frame_skip=1, num_steps=1, force_fps=False, display_screen=True)
 # Note: if you want to see you agent act in real time, set force_fps to False. But don't use this setting for learning, just for display purposes.