Initial commit

Divers/renforcement2/MountainCar_train.py

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+import gym
+import numpy as np
+import MountainCar_common
+
+env=gym.make("MountainCar-v0")
+
+# Coefficient d'apprentissage
+alpha=0.1
+# Le "discount rate"
+gamma=0.98
+
+epoch=25000
+show_every=500
+
+# Politique exploration/exploitation
+epsilon=1.
+epsilon_min=0.1
+start_epsilon=1
+end_epsilon=epoch//2
+epsilon_decay_value=epsilon/(end_epsilon-start_epsilon)
+
+nbr_action=env.action_space.n
+q_table=np.random.uniform(low=-1, high=1, size=(MountainCar_common.division+[nbr_action]))
+
+OK=0
+for episode in range(epoch):
+    obs=env.reset()
+    discrete_state=MountainCar_common.discretise(obs)
+    done=False
+
+    if episode%show_every == 0:
+        render=True
+        print("epoch {:06d}/{:06d}  reussite:{:04d}/{:04d}  epsilon={:08.6f}".format(episode, epoch, OK, show_every, epsilon))
+        OK=0
+    else:
+        render=False
+
+    while not done:
+
+        if np.random.random()>epsilon:
+            action=np.argmax(q_table[discrete_state])
+        else:
+            action=np.random.randint(nbr_action)
+
+        new_state, reward, done, info=env.step(action)
+        new_discrete_state=MountainCar_common.discretise(new_state)
+        if episode%show_every == 0:
+            env.render()
+
+        if new_state[0]>=env.goal_position:
+            reward=1
+            OK+=1
+
+        # Mise à jour de Q(s, a) avec la formule de Bellman
+        max_future_q=np.max(q_table[new_discrete_state])
+        current_q=q_table[discrete_state][action]
+        new_q=(1-alpha)*current_q+alpha*(reward+gamma*max_future_q)
+        q_table[discrete_state][action]=new_q
+
+        discrete_state=new_discrete_state
+
+    if end_epsilon>=episode>=start_epsilon:
+        epsilon-=epsilon_decay_value
+        if epsilon<epsilon_min:
+            epsilon=epsilon_min
+
+np.save("MountainCar_qtable", q_table)
+env.close()