Getting error: ValueError: too many values to unpack (expected 5)

Question

import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
import os


env = gym.make('CartPole-v0')
state_size = env.observation_space.shape[0]

action_size = env.action_space.n


batch_size = 32

n_episodes = 1000

output_dir = 'model_output/cartpole'

if not os.path.exists(output_dir):
     os.makedirs(output_dir)


class DQNAgent:
     def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size

        self.memory = deque(maxlen=2000)

        self.gamma = 0.9
        self.epsilon = 1.0
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.05

        self._learning_rate = 0.01

        self.model = self._build_model()

     def _build_model(self):

         model = Sequential()

         model.add(Dense(24, input_dim = self.state_size, activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(24,activation='relu'))
         model.add(Dense(50,activation='relu'))

         model.add(Dense(self.action_size, activation='sigmoid'))
         model.compile(loss='mse', optimizer=Adam(lr=self._learning_rate))

         return model


     def remember(self, state, action, reward, next_state, done):
        self.memory.append((self, state, action, reward, next_state, done))

     def act(self, state):
        if np.random.rand() <= self.epsilon:
           return random.randrange(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])

     def replay(self, batch_size):

         minibatch = random.sample(self.memory, batch_size)
         print(len(minibatch))
         for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma*np.amax(self.model.predict(next_state)[0]))

            target_f = self.model.predict(state)
            target_f[0][action] = target

            self.model.fit(state, target_f, epochs=1, verboss=0)

            if self.epsilon > self.epsilon_min:
               self.epsilon *= self.epsilon_decay

     def load(self,name):
         self.model.load_weights(name)

     def save(self, name):
          self.model.save_weights(name)



agent = DQNAgent(state_size, action_size)

done = False

for e in range(n_episodes):
     state = env.reset()
     state = np.reshape(state, [1, state_size])
     if agent.epsilon > agent.epsilon_min:
        agent.epsilon *= agent.epsilon_decay

     for time in range(5000):

         # env.render()
          action = agent.act(state)

          next_state, reward, done,  _ = env.step(action)

          reward = reward if not done else -10

          next_state = np.reshape(next_state, [1, state_size])

          agent.remember(state, action, reward, next_state, done)

          state = next_state

          if done:
             print("episode: {}/{}, score: {}, e: {:.2}".format(e, n_episodes, time, agent.epsilon))
             break

     if len(agent.memory) > batch_size:

        agent.replay(batch_size)

if e % 50 == 0:
    agent.save(output_dir + "weights_" + '{:04d}'.format(e) + ".hdf5")

我正在为 openai gym 中的 cartpole 环境创建算法，但我收到此错误：

回溯（最近调用最后）：文件 "C:/Users/ardao/Desktop/Ardaficial Intelligence/DQNs/CartPole.py"，第 145 行，位于 agent.replay(batch_size) 文件 "C:/Users/ardao/Desktop/Ardaficial Intelligence/DQNs/CartPole.py"，第 93 行，重放中对于状态、动作、奖励，next_state，以小批量完成： ValueError：要解压的值太多（预期为 5）

我正在学习本教程：https://www.youtube.com/watch?v=OYhFoMySoVs&t=2444s

谢谢

阿尔达

Answer 1

您刚刚添加了一个额外的自己。这应该解决它。如果你仔细想想，这个错误是很容易解释的。

要解压的值太多（预计 5 个）

在这一行中你可以看到你有 6 个。在 youtube 中验证代码显示了同样的事情。但是当你刚开始的时候，这些很容易被忽略。祝你好运，我鼓励你花点时间喘口气，下次慢慢地再看一遍。也许你可以自己解决。

 self.memory.append((state, action, reward, next_state, done))

Getting error: ValueError: too many values to unpack (expected 5)

Getting error: ValueError: too many values to unpack (expected 5)

python

python-3.x

openai-gym