diff --git a/Massaoudi/FlappyAgent.py b/Massaoudi/FlappyAgent.py
new file mode 100644
index 0000000..8ea1d2e
--- /dev/null
+++ b/Massaoudi/FlappyAgent.py
@@ -0,0 +1,105 @@
+import numpy as np
+from keras.models import load_model
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.optimizers import Adam
+import random
+from ple.games.flappybird import FlappyBird
+from ple import PLE
+model_test=load_model(r"model.h5")
+def FlappyPolicy(state, screen):
+    q=model_test.predict(np.array(list(state.values())).reshape(1,8))
+    max_Q_index = np.argmax(q)
+    action=[119,None][max_Q_index]
+    return action
+
+class Agent:
+    def __init__(self,nb_games=20000,gamma=0.99,epsilon=1,batchsize=30,buffer=80):
+        self.nb_games = nb_games
+        self.gamma = gamma # discount factor
+        self.epsilon = epsilon # epsilon-greddy
+        self.batchSize = batchsize # mini batch size
+        self.buffer = buffer
+
+    def build_model(self):
+        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=Adam(lr=1e-5))
+        return model
+    def train(self):
+        update=0 
+        replay = [] # init vector buffer
+        h=0 # current size of the vector buffer
+        game_number=0
+        model=self.build_model()
+        game = FlappyBird(graphics="fancy")
+        p = PLE(game, fps=30, frame_skip=1, num_steps=1, force_fps=False, display_screen=True)
+        p.reset_game()
+        actions=[119,None]
+        while(True):
+            p.reset_game()
+            state = game.getGameState()
+            while(not p.game_over()):
+                qval = model.predict(np.array(list(state.values())).reshape(1,8))
+                if (random.random() < self.epsilon): # choose randomly an exploration/exploitation strategy
+                    action=actions[np.random.randint(0,2)] # exploration
+                else: #choose best action from Q(s,a) values
+                    action = actions[np.argmax(qval)] # exploitation
+                #Take action, observe new state S'
+                #Observe reward and modify it
+                reward=p.act(action)
+                if reward >0:
+                    reward=15*reward
+                if reward <0:
+                    reward=2*reward
+                new_state = game.getGameState()
+                terminal=p.game_over() #boolean terminal state : True if the game is over
+                
+                #Experience replay storage
+                if (len(replay) < self.buffer): #if buffer not filled, add to it                
+                    replay.append((state, action, reward, new_state,terminal))
+                else: #if buffer full, overwrite old values
+                    if (h < (self.buffer-1)):
+                        h += 1
+                    else:
+                        h = 0
+                    replay[h] = (state, action, reward, new_state,terminal)
+                    #randomly sample our experience replay memory
+                    minibatch = random.sample(replay, self.batchSize)
+                    X_train = []
+                    y_train = []
+                    for memory in minibatch:
+                        s1, action, reward, s2,terminal = memory
+                        old_qval = model.predict(np.array(list(s1.values())).reshape(1,8), batch_size=1)
+                        newQ = model.predict(np.array(list(s2.values())).reshape(1,8), batch_size=1)
+                        maxQ = np.max(newQ)
+                        y = np.zeros((1,2))
+                        y[:] = old_qval[:]
+                        #calculate the update value for terminal and non terminal state
+                        if not terminal: #non-terminal state
+                            update = (reward + (self.gamma * maxQ))
+                        else: #terminal state
+                            update = reward
+                        action_index= 0 if action==119 else 1
+                        y[0][action_index] = update
+                        X_train.append(np.array(list(s1.values())).reshape(len(s1),))
+                        y_train.append(np.array(y).reshape(2,))
+        
+                    X_train = np.array(X_train)
+                    y_train = np.array(y_train)
+                    model.fit(X_train, y_train, batch_size=self.batchSize, nb_epoch=1, verbose=0)
+                state=new_state
+            # update exploitation / exploration strategy
+            game_number+=1
+            print("Game {}, epsilon= {}".format(game_number,self.epsilon))
+            if self.epsilon > 0.2:
+                self.epsilon -= (1.0/(self.nb_games))
+            # save the model every 1000 epochs
+            if game_number%1000 == 0:
+                model.save("model{}.h5".format(game_number//1000))
+if __name__=="__main__":
+    agent=Agent()
+    agent.train()
\ No newline at end of file
diff --git a/Massaoudi/flappy-bird-discussion.pdf b/Massaoudi/flappy-bird-discussion.pdf
new file mode 100644
index 0000000..3fe4b8c
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diff --git a/Massaoudi/model.h5 b/Massaoudi/model.h5
new file mode 100644
index 0000000..0356baa
Binary files /dev/null and b/Massaoudi/model.h5 differ
diff --git a/RandomBird/run.py b/Massaoudi/run.py
similarity index 100%
rename from RandomBird/run.py
rename to Massaoudi/run.py
diff --git a/RandomBird/FlappyAgent.py b/RandomBird/FlappyAgent.py
deleted file mode 100644
index 9f3ec84..0000000
--- a/RandomBird/FlappyAgent.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import numpy as np
-
-def FlappyPolicy(state, screen):
-    action=None
-    if(np.random.randint(0,2)<1):
-        action=119
-    return action
-
-