DQN实现坦克大战代码

DQN是深度强化学习算法，用于解决决策问题，如游戏中的行动选择。坦克大战是一个经典的游戏，可以用DQN来实现。

以下是使用Python和TensorFlow库实现DQN坦克大战游戏的代码：

1. 安装必要的库

!pip install tensorflow==2.0.0
!pip install gym==0.17.2
!pip install gym[atari]

2. 导入库

import gym
import random
import numpy as np
from collections import deque
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten
from tensorflow.keras.optimizers import Adam

3. 定义DQN的模型

def build_model(state_shape, action_shape):
    model = Sequential()
    model.add(Conv2D(32, (8, 8), strides=(4, 4), activation='relu', input_shape=state_shape))
    model.add(Conv2D(64, (4, 4), strides=(2, 2), activation='relu'))
    model.add(Conv2D(64, (3, 3), strides=(1, 1), activation='relu'))
    model.add(Flatten())
    model.add(Dense(512, activation='relu'))
    model.add(Dense(action_shape, activation='linear'))
    model.compile(loss='mse', optimizer=Adam(lr=0.0001))
    return model

4. 定义DQN的代理

class DQNAgent:
    def __init__(self, state_shape, action_shape):
        self.state_shape = state_shape
        self.action_shape = action_shape
        self.memory = deque(maxlen=2000)
        self.gamma = 0.95
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.model = build_model(state_shape, action_shape)

    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 random.randrange(self.action_shape)
        q_values = self.model.predict(state)
        return np.argmax(q_values[0])

    def replay(self, batch_size):
        if len(self.memory) < batch_size:
            return
        minibatch = 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)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

5. 训练DQN代理

env = gym.make('TankWar-ram-v0')
state_shape = env.observation_space.shape
action_shape = env.action_space.n
agent = DQNAgent(state_shape, action_shape)
batch_size = 32
num_episodes = 1000
num_steps = 500
for e in range(num_episodes):
    state = env.reset()
    state = np.reshape(state, [1, state_shape[0]])
    for step in range(num_steps):
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, state_shape[0]])
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        if done:
            break
    agent.replay(batch_size)

6. 测试DQN代理

state = env.reset()
state = np.reshape(state, [1, state_shape[0]])
for step in range(num_steps):
    env.render()
    action = agent.act(state)
    next_state, reward, done, _ = env.step(action)
    next_state = np.reshape(next_state, [1, state_shape[0]])
    state = next_state
    if done:
        break
env.close()

这就是使用DQN实现坦克大战的完整代码。您可以使用以上代码来训练和测试您自己的DQN代理。