帮我写一个DQN实现飞鸟游戏python 代码

好的，我可以回答这个问题。以下是一个简单的DQN实现飞鸟游戏的Python代码：

import random
import numpy as np
import tensorflow as tf
import pygame

# 游戏参数
SCREEN_WIDTH = 288
SCREEN_HEIGHT = 512
PIPE_GAP = 100
PIPE_VELOCITY = -4
BIRD_VELOCITY = -9
GRAVITY = 1
BIRD_WIDTH = 34
BIRD_HEIGHT = 24
PIPE_WIDTH = 52
PIPE_HEIGHT = 320
PIPE_INTERVAL = 150
PIPE_COUNT = 2
BIRD_X = 50
BIRD_Y = 200

# DQN参数
BATCH_SIZE = 32
GAMMA = 0.99
EPSILON_START = 1.0
EPSILON_END = 0.1
EPSILON_DECAY = 100000
MEMORY_SIZE = 100000
LEARNING_RATE = 0.0001
TARGET_UPDATE_FREQ = 1000

# 初始化游戏
pygame.init()
screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
pygame.display.set_caption('Flappy Bird')
clock = pygame.time.Clock()

# 加载资源
background = pygame.image.load('assets/background.png').convert()
bird_images = [pygame.image.load('assets/bird0.png').convert_alpha(),
               pygame.image.load('assets/bird1.png').convert_alpha(),
               pygame.image.load('assets/bird2.png').convert_alpha()]
pipe_image = pygame.image.load('assets/pipe.png').convert_alpha()
font = pygame.font.Font('assets/Flappy-Bird.ttf', 36)

# 定义游戏对象
class Bird:
    def __init__(self):
        self.x = BIRD_X
        self.y = BIRD_Y
        self.velocity = 0
        self.image_index = 0

    def update(self):
        self.velocity += GRAVITY
        self.y += self.velocity

        if self.y < 0:
            self.y = 0
            self.velocity = 0
        elif self.y > SCREEN_HEIGHT - BIRD_HEIGHT:
            self.y = SCREEN_HEIGHT - BIRD_HEIGHT
            self.velocity = 0

        self.image_index += 1
        if self.image_index >= len(bird_images):
            self.image_index = 0

    def jump(self):
        self.velocity = BIRD_VELOCITY

    def get_mask(self):
        return pygame.mask.from_surface(bird_images[self.image_index])

class Pipe:
    def __init__(self, x):
        self.x = x
        self.y = random.randint(PIPE_GAP, SCREEN_HEIGHT - PIPE_GAP - PIPE_HEIGHT)
        self.passed = False

    def update(self):
        self.x += PIPE_VELOCITY

    def get_mask(self):
        return pygame.mask.from_surface(pipe_image)

class Game:
    def __init__(self):
        self.bird = Bird()
        self.pipes = [Pipe(SCREEN_WIDTH + i * PIPE_INTERVAL) for i in range(PIPE_COUNT)]
        self.score = 0

    def update(self):
        self.bird.update()

        for pipe in self.pipes:
            pipe.update()

            if pipe.x < -PIPE_WIDTH:
                self.pipes.remove(pipe)
                self.pipes.append(Pipe(self.pipes[-1].x + PIPE_INTERVAL))

            if not pipe.passed and pipe.x + PIPE_WIDTH < self.bird.x:
                pipe.passed = True
                self.score += 1

            if pygame.sprite.collide_mask(self.bird, pipe):
                return False

        return True

    def jump(self):
        self.bird.jump()

    def get_state(self):
        state = np.zeros((SCREEN_HEIGHT, SCREEN_WIDTH, 3), dtype=np.uint8)
        state.fill(255)
        state[self.bird.y:self.bird.y + BIRD_HEIGHT, self.bird.x:self.bird.x + BIRD_WIDTH] = bird_images[self.bird.image_index]

        for pipe in self.pipes:
            state[pipe.y:pipe.y + PIPE_HEIGHT, pipe.x:pipe.x + PIPE_WIDTH] = pipe_image

        return state

# 定义DQN网络
class DQN:
    def __init__(self):
        self.model = tf.keras.Sequential([
            tf.keras.layers.Conv2D(32, (8, 8), strides=(4, 4), activation='relu', input_shape=(SCREEN_HEIGHT, SCREEN_WIDTH, 3)),
            tf.keras.layers.Conv2D(64, (4, 4), strides=(2, 2), activation='relu'),
            tf.keras.layers.Conv2D(64, (3, 3), strides=(1, 1), activation='relu'),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(512, activation='relu'),
            tf.keras.layers.Dense(2)
        ])

        self.target_model = tf.keras.models.clone_model(self.model)
        self.target_model.set_weights(self.model.get_weights())

        self.optimizer = tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE)
        self.loss_fn = tf.keras.losses.MeanSquaredError()

        self.memory = []

        self.epsilon = EPSILON_START

        self.steps = 0

    def act(self, state):
        if np.random.rand() < self.epsilon:
            return np.random.randint(2)
        else:
            q_values = self.model.predict(np.array([state]))[0]
            return np.argmax(q_values)

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

        if len(self.memory) > MEMORY_SIZE:
            self.memory.pop(0)

        if self.steps % TARGET_UPDATE_FREQ == 0:
            self.target_model.set_weights(self.model.get_weights())

        self.steps += 1

        self.epsilon = EPSILON_END + (EPSILON_START - EPSILON_END) * np.exp(-self.steps / EPSILON_DECAY)

    def replay(self):
        if len(self.memory) < BATCH_SIZE:
            return

        batch = random.sample(self.memory, BATCH_SIZE)

        states = np.array([sample[0] for sample in batch])
        actions = np.array([sample[1] for sample in batch])
        rewards = np.array([sample[2] for sample in batch])
        next_states = np.array([sample[3] for sample in batch])
        dones = np.array([sample[4] for sample in batch])

        q_values = self.model.predict(states)
        next_q_values = self.target_model.predict(next_states)

        for i in range(BATCH_SIZE):
            if dones[i]:
                q_values[i][actions[i]] = rewards[i]
            else:
                q_values[i][actions[i]] = rewards[i] + GAMMA * np.max(next_q_values[i])

        with tf.GradientTape() as tape:
            predictions = self.model(states)
            loss = self.loss_fn(q_values, predictions)

        gradients = tape.gradient(loss, self.model.trainable_variables)
        self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))

# 训练DQN
game = Game()
dqn = DQN()

while True:
    state = game.get_state()

    action = dqn.act(state)

    if action == 0:
        game.jump()

    done = not game.update()

    next_state = game.get_state()

    reward = game.score

    dqn.remember(state, action, reward, next_state, done)

    dqn.replay()

    screen.blit(background, (0, 0))

    for pipe in game.pipes:
        screen.blit(pipe_image, (pipe.x, pipe.y))

    screen.blit(bird_images[game.bird.image_index], (game.bird.x, game.bird.y))

    score_text = font.render(str(game.score), True, (255, 255, 255))
    screen.blit(score_text, (SCREEN_WIDTH // 2 - score_text.get_width() // 2, 50))

    pygame.display.update()

    if done:
        game = Game()

    clock.tick(60)

这是一个基于TensorFlow和Pygame的DQN实现，可以训练一个能够玩Flappy Bird游戏的AI。