我实现了Q-learning算法,并在OpenAI gym上的FrozenLake-v0上使用了它。 我在10000集的训练中获得185份奖励,在测试中获得7333份奖励。 这个好吗
我还尝试了Dyna-Q算法。但它的表现比Q-learning差。 培训期间的总奖励约为200,测试期间的总奖励约为700-900,共10000集,包含50个计划步骤
为什么会这样
下面是代码。代码有问题吗
# Setup
env = gym.make('FrozenLake-v0')
epsilon = 0.9
lr_rate = 0.1
gamma = 0.99
planning_steps = 0
total_episodes = 10000
max_steps = 100
培训和测试()
while t < max_steps:
action = agent.choose_action(state)
state2, reward, done, info = agent.env.step(action)
# Removed in testing
agent.learn(state, state2, reward, action)
agent.model.add(state, action, state2, reward)
agent.planning(planning_steps)
# Till here
state = state2
def add(self, state, action, state2, reward):
self.transitions[state, action] = state2
self.rewards[state, action] = reward
def sample(self, env):
state, action = 0, 0
# Random visited state
if all(np.sum(self.transitions, axis=1)) <= 0:
state = np.random.randint(env.observation_space.n)
else:
state = np.random.choice(np.where(np.sum(self.transitions, axis=1) > 0)[0])
# Random action in that state
if all(self.transitions[state]) <= 0:
action = np.random.randint(env.action_space.n)
else:
action = np.random.choice(np.where(self.transitions[state] > 0)[0])
return state, action
def step(self, state, action):
state2 = self.transitions[state, action]
reward = self.rewards[state, action]
return state2, reward
def choose_action(self, state):
if np.random.uniform(0, 1) < epsilon:
return self.env.action_space.sample()
else:
return np.argmax(self.Q[state, :])
def learn(self, state, state2, reward, action):
# predict = Q[state, action]
# Q[state, action] = Q[state, action] + lr_rate * (target - predict)
target = reward + gamma * np.max(self.Q[state2, :])
self.Q[state, action] = (1 - lr_rate) * self.Q[state, action] + lr_rate * target
def planning(self, n_steps):
# if len(self.transitions)>planning_steps:
for i in range(n_steps):
state, action = self.model.sample(self.env)
state2, reward = self.model.step(state, action)
self.learn(state, state2, reward, action)
我想可能是因为环境是随机的。在随机环境中学习模型可能导致次优策略。在Sutton&;巴托的书,他们说,他们假设确定性环境
检查在采取模型步骤后,下一个状态即
state2
的计划步骤样本否则,规划可能会从
self.env
给出的相同起始状态重复步骤然而,我可能误解了
self.env
参数在self.model.sample(self.env)
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