请为我提供一个DQN算法的代码
时间: 2023-03-27 20:01:01 浏览: 54
以下是一个简单的DQN算法的代码:
```python
import numpy as np
import tensorflow as tf
from collections import deque
class DQNAgent:
def __init__(self, state_size, action_size):
self.state_size = state_size
self.action_size = action_size
self.memory = deque(maxlen=200)
self.gamma = .95
self.epsilon = 1.
self.epsilon_min = .01
self.epsilon_decay = .995
self.learning_rate = .001
self.model = self._build_model()
def _build_model(self):
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))
model.add(tf.keras.layers.Dense(24, activation='relu'))
model.add(tf.keras.layers.Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
return model
def remember(self, state, action, reward, next_state, done):
self.memory.append((state, action, reward, next_state, done))
def act(self, state):
if np.random.rand() <= self.epsilon:
return np.random.choice(self.action_size)
act_values = self.model.predict(state)
return np.argmax(act_values[])
def replay(self, batch_size):
minibatch = np.array(random.sample(self.memory, batch_size))
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)[]))
target_f = self.model.predict(state)
target_f[][action] = target
self.model.fit(state, target_f, epochs=1, verbose=)
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
```
请问,这个代码是用来干什么的?