DQN（Deep Q-Network）是一种基于深度学习的强化学习算法。它通过神经网络估计每个动作的Q值，从而实现智能体对环境的决策。下面是DQN算法的Python实现步骤：
1.导入必要的库和环境
```python
import gym
import numpy as np
import tensorflow as tf
from collections import deque
env = gym.make('CartPole-v0')
state_size = env.observation_space.shape
action_size = env.action_space.n
```
2.定义DQN模型，包括网络结构和训练方法
```python
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 = 0.95
self.epsilon = 1.0
self.epsilon_decay = 0.995
self.epsilon_min = 0.01
self.learning_rate = 0.001
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=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)
```
3.训练DQN模型并测试
```python
agent = DQNAgent(state_size, action_size)
done = False
batch_size = 32
EPISODES = 100
for e in range(EPISODES):
state = env.reset()
state = np.reshape(state, [1, state_size])
for time in range(500):
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, EPISODES, time, agent.epsilon))
break
if len(agent.memory) > batch_size:
agent.replay(batch_size)
if e % 10 == 0:
agent.save("./dqn.h5")
# test the trained DQN model
agent.load("./dqn.h5")
state = env.reset()
state = np.reshape(state, [1, state_size])
for time in range(500):
env.render()
action = agent.act(state)
next_state, reward, done, _ = env.step(action)
state = np.reshape(next_state, [1, state_size])
if done:
break
env.close()
```
订阅专栏 解锁全文
扫一扫
455
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
