83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
import gym
|
|
import numpy as np
|
|
import cv2
|
|
import CartPole_common
|
|
|
|
env=gym.make("CartPole-v0")
|
|
env._max_episode_steps=500
|
|
|
|
alpha=0.05
|
|
gamma=0.98
|
|
|
|
epoch=50000
|
|
show_every=500
|
|
|
|
epsilon=1.
|
|
epsilon_min=0.05
|
|
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=(CartPole_common.division+[nbr_action]))
|
|
|
|
result_done=0
|
|
scores=[]
|
|
best_score=0
|
|
for episode in range(epoch):
|
|
obs=env.reset()
|
|
discrete_state=CartPole_common.discretise(obs)
|
|
done=False
|
|
|
|
if episode%show_every == 0:
|
|
render=True
|
|
mean_score=np.mean(scores)
|
|
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))
|
|
scores=[]
|
|
result_done=0
|
|
if mean_score>best_score:
|
|
print("Sauvegarde ...")
|
|
np.save("CartPole_qtable", q_table)
|
|
best_score=mean_score
|
|
alpha=alpha*0.99
|
|
|
|
else:
|
|
render=False
|
|
|
|
score=1
|
|
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=CartPole_common.discretise(new_state)
|
|
|
|
if episode%show_every == 0:
|
|
env.render()
|
|
|
|
#reward=2-np.abs(new_state[0])
|
|
if done:
|
|
scores.append(score)
|
|
if score==env._max_episode_steps:
|
|
result_done+=1
|
|
else:
|
|
reward=-10
|
|
|
|
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
|
|
|
|
score+=1
|
|
discrete_state=new_discrete_state
|
|
|
|
if end_epsilon>=episode>=start_epsilon:
|
|
epsilon-=epsilon_decay_value
|
|
if epsilon<epsilon_min:
|
|
epsilon=epsilon_min
|
|
|
|
env.close()
|