输出结果
设计思路
实现代码
from __future__ import print_function
import numpy as np
import time
from env import Env
EPSILON = 0.1
ALPHA = 0.1
GAMMA = 0.9
MAX_STEP = 30
np.random.seed(0)
def epsilon_greedy(Q, state):
if (np.random.uniform() > 1 - EPSILON) or ((Q[state, :] == 0).all()):
action = np.random.randint(0, 4) # 0~3
else:
action = Q[state, :].argmax()
return action
e = Env()
Q = np.zeros((e.state_num, 4))
for i in range(200):
e = Env()
while (e.is_end is False) and (e.step < MAX_STEP):
action = epsilon_greedy(Q, e.present_state)
state = e.present_state
reward = e.interact(action)
new_state = e.present_state
Q[state, action] = (1 - ALPHA) * Q[state, action] + \
ALPHA * (reward + GAMMA * Q[new_state, :].max())
e.print_map()
time.sleep(0.1)
print('Episode:', i, 'Total Step:', e.step, 'Total Reward:', e.total_reward)
time.sleep(2)
