我要DQN算法的python代码
时间: 2023-10-03 17:05:45 浏览: 93
下面是一个简单的DQN算法实现的Python代码:
```python
import tensorflow as tf
import numpy as np
class DQN:
def __init__(self, state_dim, action_dim, gamma, learning_rate):
self.state_dim = state_dim
self.action_dim = action_dim
self.gamma = gamma
self.learning_rate = learning_rate
self.memory = []
self.model = tf.keras.Sequential([
tf.keras.layers.Dense(32, input_shape=(self.state_dim,), activation='relu'),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(self.action_dim, activation=None)
])
self.optimizer = tf.keras.optimizers.Adam(lr=self.learning_rate)
def remember(self, state, action, reward, next_state, done):
self.memory.append([state, action, reward, next_state, done])
def act(self, state, epsilon):
if np.random.rand() < epsilon:
return np.random.randint(self.action_dim)
else:
q_values = self.model.predict(state)
return np.argmax(q_values[0])
def replay(self, batch_size):
if len(self.memory) < batch_size:
return
samples = np.array(random.sample(self.memory, batch_size))
states = np.vstack(samples[:, 0])
actions = samples[:, 1]
rewards = samples[:, 2]
next_states = np.vstack(samples[:, 3])
dones = samples[:, 4]
targets = rewards + self.gamma * np.max(self.model.predict(next_states), axis=1) * (1 - dones)
q_values = self.model.predict(states)
for i in range(batch_size):
q_values[i][actions[i]] = targets[i]
self.model.fit(states, q_values, verbose=0)
```
这是一个简单的DQN类,包含remember、act、replay等方法,可以根据需要进行调整。其中,act方法用来根据当前状态选择动作,replay方法用来对记忆库中的样本进行训练。
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