利用pytorch实现遗传算法，用于实现贪吃蛇游戏，请给出具体代码

好的，以下是一个简单的利用PyTorch实现遗传算法的贪吃蛇游戏示例代码：

import random
import numpy as np
import torch

# 游戏相关参数
WIDTH = 20
HEIGHT = 20
FOOD_REWARD = 10
DEATH_PENALTY = -10
MOVE_PENALTY = -1

class SnakeGame:
    def __init__(self):
        self.width = WIDTH
        self.height = HEIGHT
        self.food_reward = FOOD_REWARD
        self.death_penalty = DEATH_PENALTY
        self.move_penalty = MOVE_PENALTY
        self.reset()

    def reset(self):
        self.snake = [(0, 0)]
        self.food = self.generate_food()
        self.direction = 0
        self.score = 0
        self.steps = 0

    def generate_food(self):
        while True:
            food = (random.randint(0, self.width-1), random.randint(0, self.height-1))
            if food not in self.snake:
                return food

    def get_state(self):
        head = self.snake[0]
        left = (head[0]-1, head[1])
        right = (head[0]+1, head[1])
        up = (head[0], head[1]-1)
        down = (head[0], head[1]+1)

        state = [
            int((left in self.snake or left[0] < 0) and self.direction != 1),  # danger straight
            int((right in self.snake or right[0] >= self.width) and self.direction != 0),  # danger straight
            int((up in self.snake or up[1] < 0) and self.direction != 3),  # danger straight
            int((down in self.snake or down[1] >= self.height) and self.direction != 2),  # danger straight
            int(self.direction == 0 and down in self.snake),  # danger right
            int(self.direction == 0 and up in self.snake),  # danger left
            int(self.direction == 1 and left in self.snake),  # danger right
            int(self.direction == 1 and up in self.snake),  # danger left
            int(self.direction == 2 and up in self.snake),  # danger right
            int(self.direction == 2 and right in self.snake),  # danger left
            int(self.direction == 3 and right in self.snake),  # danger right
            int(self.direction == 3 and down in self.snake),  # danger left
            self.food[0] - head[0],  # food x distance
            self.food[1] - head[1]  # food y distance
        ]

        return np.array(state, dtype=int)

    def play_step(self, action):
        self.steps += 1
        reward = self.move_penalty

        if action == 0:
            new_head = (self.snake[0][0], self.snake[0][1]-1)
        elif action == 1:
            new_head = (self.snake[0][0], self.snake[0][1]+1)
        elif action == 2:
            new_head = (self.snake[0][0]-1, self.snake[0][1])
        else:
            new_head = (self.snake[0][0]+1, self.snake[0][1])

        if new_head == self.food:
            self.score += self.food_reward
            self.snake.insert(0, new_head)
            self.food = self.generate_food()
            reward = self.food_reward
        elif new_head[0] < 0 or new_head[0] >= self.width or new_head[1] < 0 or new_head[1] >= self.height or new_head in self.snake:
            self.score += self.death_penalty
            reward = self.death_penalty
            self.reset()
        else:
            self.snake.insert(0, new_head)
            self.snake.pop()
            reward = self.move_penalty

        self.direction = self.get_direction()
        state = self.get_state()
        done = False

        return state, reward, done

    def get_direction(self):
        dx = self.snake[0][0] - self.snake[1][0]
        dy = self.snake[0][1] - self.snake[1][1]

        if dx == 0:
            return 0 if dy == -1 else 1
        else:
            return 2 if dx == -1 else 3

class GeneticAlgorithm:
    def __init__(self, population_size, mutation_rate, model_fn):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.model_fn = model_fn

        self.population = [model_fn() for _ in range(population_size)]
        self.fitness = [0 for _ in range(population_size)]

    def select(self):
        parent1_idx = random.choices(range(self.population_size), weights=self.fitness)[0]
        parent2_idx = random.choices(range(self.population_size), weights=self.fitness)[0]
        return parent1_idx, parent2_idx

    def crossover(self, parent1, parent2):
        child1 = parent1.clone()
        child2 = parent2.clone()

        for param1, param2 in zip(child1.parameters(), child2.parameters()):
            mask = torch.empty_like(param1).uniform_() < 0.5
            param1[mask], param2[mask] = param2[mask], param1[mask]

        return child1, child2

    def mutate(self, model):
        for param in model.parameters():
            mask = torch.empty_like(param).uniform_() < self.mutation_rate
            delta = torch.empty_like(param).normal_(0, 0.1)
            param[mask] += delta[mask]

    def evolve(self):
        # 计算适应度
        for i, model in enumerate(self.population):
            fitness = self.evaluate(model)
            self.fitness[i] = fitness

        # 选择与繁殖新一代
        new_population = []
        for _ in range(self.population_size):
            parent1_idx, parent2_idx = self.select()
            parent1 = self.population[parent1_idx]
            parent2 = self.population[parent2_idx]
            child1, child2 = self.crossover(parent1, parent2)
            self.mutate(child1)
            self.mutate(child2)
            new_population.extend([child1, child2])

        self.population = new_population

    def evaluate(self, model):
        game = SnakeGame()
        state = game.get_state()
        done = False
        fitness = 0

        while not done:
            action = model(torch.tensor(state).float().unsqueeze(0)).argmax(dim=1).item()
            state, reward, done = game.play_step(action)
            fitness += reward

        return fitness

    def train(self, num_generations):
        for generation in range(num_generations):
            self.evolve()
            fitness = self.fitness
            print(f"Generation: {generation}, Max Fitness: {max(fitness)}, Avg Fitness: {sum(fitness) / len(fitness)}")

if __name__ == "__main__":
    # 定义模型
    model = torch.nn.Sequential(
        torch.nn.Linear(13, 32),
        torch.nn.ReLU(),
        torch.nn.Linear(32, 16),
        torch.nn.ReLU(),
        torch.nn.Linear(16, 4)
    )

    # 定义遗传算法
    ga = GeneticAlgorithm(population_size=50, mutation_rate=0.1, model_fn=lambda: model.clone())

    # 训练遗传算法
    ga.train(num_generations=100)

代码中定义了一个 `SnakeGame` 类来实现贪吃蛇游戏，并实现了一个 `GeneticAlgorithm` 类来实现遗传算法。其中，`GeneticAlgorithm` 类的 `model_fn` 参数是一个函数，用于初始化模型，本例中使用了一个简单的前馈神经网络作为模型。在 `GeneticAlgorithm` 类的 `train` 方法中，首先计算每个个体的适应度，然后进行选择、交叉和变异，生成新一代个体。在训练过程中，会输出每一代的最大适应度和平均适应度。