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
from reprint import output
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))
with output(output_type="list", initial_len=len(e.map), interval=0) as output_list:
for i in range(100):
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_with_reprint(output_list)
time.sleep(0.1)
for line_num in range(len(e.map)):
if line_num == 0:
output_list[0] = 'Episode:{} Total Step:{}, Total Reward:{}'.format(i, e.step, e.total_reward)
else:
output_list[line_num] = ''
time.sleep(2)
