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