Python 深度Q学习-训练速度明显减慢

我正在尝试建立一个深度Q网络来玩snake。我设计的游戏窗口是600乘600，蛇头每滴答移动30像素。我使用内存重播和目标网络实现了DQN算法，但一旦策略网络开始更新其权重，训练就会显著减慢，每次迭代权重更新循环大约需要5分钟。此外，我几乎看不到该经纪人的表现有任何改善，即使在接受了大约500集的训练之后。以下是代理的代码：

import numpy as np
import tensorflow as tf
from snake_rl.envs.snake_env import SnakeEnv
import random
from Game.experience import Experience
import time
import pygame
from PIL import Image
from keras import Sequential
from keras.layers import Conv2D, Dense, BatchNormalization, Activation, Flatten, Reshape
import matplotlib.pyplot as plt
import matplotlib.image as mpimg


class Brain:
    def __init__(self, learning_rate, discount_rate, eps_start, eps_end, eps_decay, memory_size, batch_size, max_episodes, max_steps, target_update):
        self.memory = []
        self.push_count = 0
        self.learning_rate = learning_rate
        self.discount_rate = discount_rate
        self.eps_start = eps_start
        self.current_eps = eps_start
        self.eps_end = eps_end
        self.eps_decay = eps_decay
        self.memory_size = memory_size
        self.batch_size = batch_size
        self.max_steps = max_steps
        self.max_episodes = max_episodes
        self.current_episode = 1
        self.policy_model = None
        self.replay_model = None
        self.target_update = target_update
        pygame.init()
        self.screen = pygame.display.set_mode((600, 600))
        pygame.display.set_caption("Snake")       

    def build_model(self):
        self.policy_model = Sequential()
        self.policy_model.add(Conv2D(8, (5, 5), padding = 'same', activation = 'relu', data_format = "channels_last", input_shape = (600, 600, 2)))
        self.policy_model.add(Conv2D(16, (5, 5), padding="same", activation="relu"))
        self.policy_model.add(Conv2D(32, (5, 5), padding="same", activation="relu"))
        self.policy_model.add(Flatten())
        self.policy_model.add(Dense(16, activation = "relu"))
        self.policy_model.add(Dense(5, activation = "softmax"))
        self.policy_model.compile(optimizer = 'rmsprop', loss = 'mean_squared_error')

        self.replay_model = Sequential()
        self.replay_model.add(Conv2D(8, (5, 5), padding = 'same', activation = 'relu', data_format = "channels_last", input_shape = (600, 600, 2)))
        self.replay_model.add(Conv2D(16, (5, 5), padding="same", activation="relu"))
        self.replay_model.add(Conv2D(32, (5, 5), padding="same", activation="relu"))
        self.replay_model.add(Flatten())
        self.replay_model.add(Dense(16, activation = "relu"))
        self.replay_model.add(Dense(5, activation = "softmax"))
        self.replay_model.compile(optimizer = 'rmsprop', loss = 'mean_squared_error')
        print(self.policy_model.summary())

    def decay_epsilon(self, episode):
        self.current_eps = self.eps_end + (self.eps_start - self.eps_end) * np.exp(-self.eps_decay * episode)

    def push_memory(self, new_memory):
        if(len(self.memory) < self.memory_size):
            self.memory.append(new_memory)
        else:
            self.memory[self.push_count % self.memory_size] = new_memory
        self.push_count += 1

    def sample_memory(self):
        return random.sample(self.memory, self.batch_size)

    def can_sample_memory(self):
        return len(self.memory) >= self.batch_size

    def screenshot(self):
        data = pygame.image.tostring(self.screen, 'RGB')
        image = Image.frombytes('RGB', (600, 600), data)
        image = image.convert('LA')
        matrix = np.asarray(image.getdata(), dtype=np.uint8)
        matrix = (matrix - 128)/(128 - 1)
        matrix = np.reshape(matrix, (1, 600, 600, 2))
        return matrix

    def train(self):
        tf.logging.set_verbosity(tf.logging.ERROR)
        self.build_model()
        for episode in range(self.max_episodes):
            self.current_episode = episode
            env = SnakeEnv(self.screen)
            episode_reward = 0
            for timestep in range(self.max_steps):
                env.render(self.screen)
                state = self.screenshot()
                #state = env.get_state()
                action = None
                epsilon = self.current_eps
                if epsilon > random.random():
                    action = np.random.choice(env.action_space) #explore
                else:
                    values = self.policy_model.predict(state) #exploit
                    action = np.argmax(values)
                experience = env.step(action)
                if(experience['done'] == True):
                    episode_reward += experience['reward']
                    break
                episode_reward += experience['reward']
                self.push_memory(Experience(experience['state'], experience['action'], experience['reward'], experience['next_state']))
                self.decay_epsilon(episode)
                if self.can_sample_memory():
                    memory_sample = self.sample_memory()
                    X = []
                    Y = []
                    for memory in memory_sample:
                        memstate = memory.state
                        action = memory.action
                        next_state = memory.next_state
                        reward = memory.reward
                        max_q = reward + (self.discount_rate * self.replay_model.predict(next_state)) #bellman equation
                        X.append(memstate)
                        Y.append(max_q)
                    X = np.array(X)
                    X = X.reshape([-1, 600, 600, 2])
                    Y = np.array(Y)
                    Y = Y.reshape([128, 5])
                    self.policy_model.fit(X, Y)
            print("Episode: ", episode, " Total Reward: ", episode_reward)
            if episode % self.target_update == 0:
                self.replay_model.set_weights(self.policy_model.get_weights())
        self.policy_model.save_weights('weights.hdf5')
        pygame.quit()

    def render(self):
        self.env.render(self.screen)

    def choose_action(self, state):
        q_values = self.policy_model.predict(state)
        action = np.amax(q_values)
        return action

    def load(self):
        self.build_model()
        self.policy_model.load_weights("weights.hdf5")

    def play(self):
        for episode in range(100):
            env = SnakeEnv(self.screen)
            for timestep in range(1000):
                env.render(self.screen)
                pred = self.policy_model.predict(env.get_state())
                print(np.array(pred))
                action = np.amax(pred)
                d = env.step(action)
                if(d['done'] == True):
                    break

有人对如何加快培训和提高绩效有什么建议吗

def decay_epsilon(self, episode):
    self.current_eps = self.eps_end + (self.eps_start - self.eps_end) * np.exp(-self.eps_decay * episode)



// part of code from train()
epsilon = self.current_eps
            if epsilon > random.random():
                action = np.random.choice(env.action_space) #explore
            else:
                values = self.policy_model.predict(state) #exploit
                action = np.argmax(values)

问题 当情节从1增加到1000时，采取随机行动的可能性从100%减少到36%。当剧集数为500集时，有40%的几率采取随机行动

我的解决方案

等等。3000集，占随机动作的5%

eps_衰减=0.006。500集时，随机动作减少到5%

---

将decay改为.006，可能会让它训练一整天，看看会发生什么

def decay_epsilon(self, episode):
    self.current_eps = self.eps_end + (self.eps_start - self.eps_end) * np.exp(-self.eps_decay * episode)



// part of code from train()
epsilon = self.current_eps
            if epsilon > random.random():
                action = np.random.choice(env.action_space) #explore
            else:
                values = self.policy_model.predict(state) #exploit
                action = np.argmax(values)