我正在尝试解决openAI健身房中的难题。通过Q学习。我认为我误解了Q学习的工作原理,因为我的模型没有得到改善。
我使用字典作为我的Q表。因此,我每次观察都会“散列”(变成字符串)。并以此作为我表中的键。
我表中的每个键(观察值)都映射到另一个字典。我将在此状态下执行的每个移动及其关联的Q值存储在此处。
话虽如此,我表中的条目可能如下所示:
'[''0.102'', ''1.021'', ''-0.133'', ''-1.574'']':
0: 0.1
因此处于状态(观察):
'[''0.102'', ''1.021'', ''-0.133'', ''-1.574'']'
记录了一个动作:
0
,其q值为:0.01
。我的逻辑错了吗?我真的无法弄清楚我的实现在哪里出错。
import gym
import random
import numpy as np
ENV = 'CartPole-v0'
env = gym.make(ENV)
class Qtable:
def __init__(self):
self.table = {}
def update_table(self, obs, action, value):
obs_hash = self.hash_obs(obs)
# Update table with new observation
if not obs_hash in self.table:
self.table[obs_hash] = {}
self.table[obs_hash][action] = value
else:
# Check if action has been recorded
# If such, check if this value was better
# If not, record new action for this obs
if action in self.table[obs_hash]:
if value > self.table[obs_hash][action]:
self.table[obs_hash][action] = value
else:
self.table[obs_hash][action] = value
def get_prev_value(self, obs, action):
obs_hash = self.hash_obs(obs)
if obs_hash in self.table:
if action in self.table[obs_hash]:
return self.table[obs_hash][action]
return 0
def get_max_value(self, obs):
obs_hash = self.hash_obs(obs)
if obs_hash in self.table:
key = max(self.table[obs_hash])
return self.table[obs_hash][key]
return 0
def has_action(self, obs):
obs_hash = self.hash_obs(obs)
if obs_hash in self.table:
if len(self.table[obs_hash]) > 0:
return True
return False
def get_best_action(self, obs):
obs_hash = self.hash_obs(obs)
if obs_hash in self.table:
return max(self.table[obs_hash])
# Makes a hashable entry of the observation
def hash_obs(self, obs):
return str(['{:.3f}'.format(i) for i in obs])
def play():
q_table = Qtable()
# Hyperparameters
alpha = 0.1
gamma = 0.6
epsilon = 0.1
episodes = 1000
total = 0
for i in range(episodes):
done = False
prev_obs = env.reset()
episode_reward = 0
while not done:
if random.uniform(0, 1) > epsilon and q_table.has_action(prev_obs):
# Exploit learned values
action = q_table.get_best_action(prev_obs)
else:
# Explore action space
action = env.action_space.sample()
# Render the environment
#env.render()
# Take a step
obs, reward, done, info = env.step(action)
if done:
reward = -200
episode_reward += reward
old_value = q_table.get_prev_value(prev_obs, action)
next_max = q_table.get_max_value(obs)
# Get the current sate value
new_value = (1-alpha)*old_value + alpha*(reward + gamma*next_max)
q_table.update_table(obs, action, new_value)
prev_obs = obs
total += episode_reward
print("average", total/episodes)
env.close()
play()
最佳答案
我想我知道了。我误会了这部分new_value = (1-alpha)*old_value + alpha*(reward + gamma*next_max)
next_max
是下一个状态的最佳动作。而不是(应该是)此子树的最大值。
因此,将Q表实现为哈希表可能不是一个好主意。
关于python - Q学习模式没有改善,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/54708749/