83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
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import gym
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import numpy as np
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import cv2
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import CartPole_common
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env=gym.make("CartPole-v0")
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env._max_episode_steps=500
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alpha=0.05
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gamma=0.98
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epoch=50000
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show_every=500
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epsilon=1.
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epsilon_min=0.05
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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=(CartPole_common.division+[nbr_action]))
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result_done=0
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scores=[]
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best_score=0
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for episode in range(epoch):
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obs=env.reset()
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discrete_state=CartPole_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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mean_score=np.mean(scores)
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print("Epoch {:06d}/{:06d} reussite:{:04d}/{:04d} epsilon={:06.4f} Mean score={:08.4f} alpha={:06.4f}".format(episode, epoch, result_done, show_every, epsilon, mean_score, alpha))
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scores=[]
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result_done=0
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if mean_score>best_score:
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print("Sauvegarde ...")
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np.save("CartPole_qtable", q_table)
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best_score=mean_score
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alpha=alpha*0.99
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else:
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render=False
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score=1
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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=CartPole_common.discretise(new_state)
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if episode%show_every == 0:
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env.render()
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#reward=2-np.abs(new_state[0])
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if done:
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scores.append(score)
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if score==env._max_episode_steps:
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result_done+=1
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else:
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reward=-10
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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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score+=1
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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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env.close()
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