深度 Q 学习 - 训练显着减慢

Question

我正在尝试建立一个深度 Q 网络来玩蛇。我将游戏设计为使窗口为 600 x 600，并且蛇的头部每刻移动 30 个像素。我使用内存重放和目标网络实现了 DQN 算法，但是一旦策略网络开始更新其权重，训练就会显着减慢，以至于权重更新循环的每次迭代大约需要 5 分钟。此外，即使在训练了大约 500 集之后，我也发现代理的表现几乎没有任何改善。这是代理的代码：

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

我的超参数如下：

learning_rate = 0.5
discount_rate = 0.99
eps_start = 1
eps_end = .01
eps_decay = .001
memory_size = 100000
batch_size = 128
max_episodes = 1000
max_steps = 5000
target_update = 10

有人对如何加快训练和提高性能有任何建议吗？

Answer 1

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% 降低到 36%。当集数为 500 时，有 40% 的机会采取随机行动。

我的解决方案

1. 等等。 3000 集使其成为随机动作的 5%。

2. eps_decay = 0.006。 500 集时，随机动作减少到 5%。