我尝试使用DQN训练一个cartpole，这是一个强化学习的例子。
但我有个运行错误。
此时

target = self.model.predict(states)

我不明白为什么会发生这种情况，即使查看描述该函数功能的文档也是如此。
网址：https://www.tensorflow.org/api_docs/python/tf/keras/Model#predict
模型声明是否无效？
有人知道解决办法吗？
我需要帮助
版本：keras 2.10.0，python 3.10.13，gym 0.26.2
下面是所有的代码

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

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

        self.state_size = state_size
        self.action_size = action_size

        self.discount_factor = 0.99
        self.learning_rate = 0.001
        self.epsilon = 1.0
        self.epsilon_decay = 0.999
        self.epsilon_min = 0.01
        self.batch_size = 64
        self.train_start = 1000

        self.memory = deque(maxlen=2000)

        self.model = self.build_model()
        self.target_model = self.build_model()

        self.update_target_model()

    def build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu', kernel_initializer='he_uniform'))
        model.add(Dense(24, activation='relu', kernel_initializer='he_uniform'))
        model.add(Dense(self.action_size, activation='linear', kernel_initializer='he_uniform'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

    def update_target_model(self):
        self.target_model.set_weights(self.model.get_weights())

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

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

    def train_model(self):
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay
 
        mini_batch = random.sample(self.memory, self.batch_size)

        states = np.zeros((self.batch_size, self.state_size))
        next_states = np.zeros((self.batch_size, self.state_size))
        actions, rewards, dones = [], [], []

        for i in range(self.batch_size):
            states[i] = mini_batch[i][0]
            actions.append(mini_batch[i][1])
            rewards.append(mini_batch[i][2])
            next_states[i] = mini_batch[i][3]
            dones.append(mini_batch[i][4])

        #error
        target = self.model.predict(states)
        target_val = self.target_model.predict(next_states)

        for i in range(self.batch_size):
            if dones[i]:
                target[i][actions[i]] = rewards[i]
            else:
                target[i][actions[i]] = rewards[i] + self.discount_factor * (np.amax(target_val[i]))

        self.model.fit(states, target, batch_size=self.batch_size, epochs=1, verbose=0)

if __name__ == "__main__":
    env = gym.make("CartPole-v1", render_mode="human")
    random_seed = 82
    env.action_space.seed(random_seed)
    state_size = env.observation_space.shape[0]
    observation, info = env.reset(seed=random_seed)
    EPISODES = 1000

    agent = DQNAgent(state_size, env.action_space.n)
    scores, episodes = [], []

    for e in range(EPISODES):
        done = False
        score = 0
        state = env.reset()[0]

        while not done:

            action = agent.get_action(state)
            next_state, reward, done, _, _ = env.step(action)
            reward = reward if not done or score == 499 else -100

            agent.append_sample(state, action, reward, next_state, done)
            if len(agent.memory) >= agent.train_start:
                agent.train_model()

            score += reward
            state = next_state

            if done:
                agent.update_target_model()

                score = score if score == 500 else score + 100
                scores.append(score)
                episodes.append(e)
                print("episode:", e, "  score:", score, "  memory length:",
                      len(agent.memory), "  epsilon:", agent.epsilon)

                if np.mean(scores[-min(10, len(scores)):]) > 490:
                    sys.exit()

这是错误：

Traceback (most recent call last):
  File "C:\Users\cglab\Desktop\Match3_DQN\main.py", line 101, in <module>
    action = agent.get_action(state)
  File "C:\Users\cglab\Desktop\Match3_DQN\main.py", line 48, in get_action
    q_value = self.model.predict(state)
  File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:

Detected at node 'sequential/dense/MatMul' defined at (most recent call last):
    File "C:\Users\cglab\Desktop\Match3_DQN\main.py", line 101, in <module>
      action = agent.get_action(state)
    File "C:\Users\cglab\Desktop\Match3_DQN\main.py", line 48, in get_action
      q_value = self.model.predict(state)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\training.py", line 2253, in predict
      tmp_batch_outputs = self.predict_function(iterator)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\training.py", line 2041, in predict_function
      return step_function(self, iterator)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\training.py", line 2027, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\training.py", line 2015, in run_step
      outputs = model.predict_step(data)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\training.py", line 1983, in predict_step
      return self(x, training=False)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\training.py", line 557, in __call__
      return super().__call__(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\base_layer.py", line 1097, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\sequential.py", line 410, in call
      return super().call(inputs, training=training, mask=mask)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\functional.py", line 510, in call
      return self._run_internal_graph(inputs, training=training, mask=mask)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\functional.py", line 667, in _run_internal_graph
      outputs = node.layer(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\engine\base_layer.py", line 1097, in __call__
      outputs = call_fn(inputs, *args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
      return fn(*args, **kwargs)
    File "c:\Users\cglab\anaconda3\envs\test2\lib\site-packages\keras\layers\core\dense.py", line 241, in call
      outputs = tf.matmul(a=inputs, b=self.kernel)
Node: 'sequential/dense/MatMul'
In[0] and In[1] has different ndims: [4] vs. [4,24]
         [[{{node sequential/dense/MatMul}}]] [Op:__inference_predict_function_14218]

欢迎所有评论。谢谢你。