参考学习地址
测试代码:
import numpy as np
import pandas as pd
import time
N_STATES = 6 # 1维世界的宽度
ACTIONS = ['left', 'right'] # 探索者的可用动作
EPSILON = 0.9 # 贪婪度 greedy
ALPHA = 0.1 # 学习率
GAMMA = 0.9 # 奖励递减值
MAX_EPISODES = 13 # 最大回合数
FRESH_TIME = 0.3 # 移动间隔时间
def build_q_table(n_states, actions):
table = pd.DataFrame(
np.zeros((n_states, len(actions))), # q_table 全 0 初始
columns=actions, # columns 对应的是行为名称
)
return table
# q_table:
"""
left right
0 0.0 0.0
1 0.0 0.0
2 0.0 0.0
3 0.0 0.0
4 0.0 0.0
5 0.0 0.0
"""
# 在某个 state 地点, 选择行为
def choose_action(state, q_table):
state_actions = q_table.iloc[state, :] # 选出这个 state 的所有 action 值
if (np.random.uniform() > EPSILON) or (state_actions.all() == 0): # 非贪婪 or 或者这个 state 还没有探索过
action_name = np.random.choice(ACTIONS)
else:
action_name = state_actions.argmax() # 贪婪模式
return action_name
def get_env_feedback(S, A):
# This is how agent will interact with the environment
if A == 'right': # move right
if S == N_STATES - 2: # terminate
S_ = 'terminal'
R = 1
else:
S_ = S + 1
R = 0
else: # move left
R = 0
if S == 0:
S_ = S # reach the wall
else:
S_ = S - 1
return S_, R
def update_env(S, episode, step_counter):
# This is how environment be updated
env_list = ['-']*(N_STATES-1) + ['T'] # '---------T' our environment
if S == 'terminal':
interaction = 'Episode %s: total_steps = %s' % (episode+1, step_counter)
print('\r{}'.format(interaction), end='')
time.sleep(2)
print('\r ', end='')
else:
env_list[S] = 'o'
interaction = ''.join(env_list)
print('\r{}'.format(interaction), end='')
time.sleep(FRESH_TIME)
def rl():
q_table = build_q_table(N_STATES, ACTIONS) # 初始 q table
for episode in range(MAX_EPISODES): # 回合
step_counter = 0
S = 0 # 回合初始位置
is_terminated = False # 是否回合结束
update_env(S, episode, step_counter) # 环境更新
while not is_terminated:
A = choose_action(S, q_table) # 选行为
S_, R = get_env_feedback(S, A) # 实施行为并得到环境的反馈
q_predict = q_table.loc[S, A] # 估算的(状态-行为)值
if S_ != 'terminal':
q_target = R + GAMMA * q_table.iloc[S_, :].max() # 实际的(状态-行为)值 (回合没结束)
else:
q_target = R # 实际的(状态-行为)值 (回合结束)
is_terminated = True # terminate this episode
q_table.loc[S, A] += ALPHA * (q_target - q_predict) # q_table 更新
S = S_ # 探索者移动到下一个 state
update_env(S, episode, step_counter+1) # 环境更新
step_counter += 1
return q_table
if __name__ == "__main__":
q_table = rl()
print('\r\nQ-table:\n')
print(q_table)