时间戳

首页 > 解决方案 > OpenAI 健身房的月球着陆器模型未收敛

neural-network - OpenAI 健身房的月球着陆器模型未收敛

问题描述

我正在尝试使用 keras 的深度强化学习来训练代理学习如何玩Lunar Lander OpenAI 健身房环境。问题是我的模型没有收敛。这是我的代码：

import numpy as np
import gym

from keras.models import Sequential
from keras.layers import Dense
from keras import optimizers

def get_random_action(epsilon):
    return np.random.rand(1) < epsilon

def get_reward_prediction(q, a):
    qs_a = np.concatenate((q, table[a]), axis=0)
    x = np.zeros(shape=(1, environment_parameters + num_of_possible_actions))
    x[0] = qs_a
    guess = model.predict(x[0].reshape(1, x.shape[1]))
    r = guess[0][0]
    return r

results = []
epsilon = 0.05
alpha = 0.003
gamma = 0.3
environment_parameters = 8
num_of_possible_actions = 4
obs = 15
mem_max = 100000
epochs = 3
total_episodes = 15000

possible_actions = np.arange(0, num_of_possible_actions)
table = np.zeros((num_of_possible_actions, num_of_possible_actions))
table[np.arange(num_of_possible_actions), possible_actions] = 1

env = gym.make('LunarLander-v2')
env.reset()

i_x = np.random.random((5, environment_parameters + num_of_possible_actions))
i_y = np.random.random((5, 1))

model = Sequential()
model.add(Dense(512, activation='relu', input_dim=i_x.shape[1]))
model.add(Dense(i_y.shape[1]))

opt = optimizers.adam(lr=alpha)

model.compile(loss='mse', optimizer=opt, metrics=['accuracy'])

total_steps = 0
i_x = np.zeros(shape=(1, environment_parameters + num_of_possible_actions))
i_y = np.zeros(shape=(1, 1))

mem_x = np.zeros(shape=(1, environment_parameters + num_of_possible_actions))
mem_y = np.zeros(shape=(1, 1))
max_steps = 40000

for episode in range(total_episodes):
    g_x = np.zeros(shape=(1, environment_parameters + num_of_possible_actions))
    g_y = np.zeros(shape=(1, 1))
    q_t = env.reset()
    episode_reward = 0

    for step_number in range(max_steps):
        if episode < obs:
            a = env.action_space.sample()
        else:
            if get_random_action(epsilon, total_episodes, episode):
                a = env.action_space.sample()
            else:
                actions = np.zeros(shape=num_of_possible_actions)

                for i in range(4):
                    actions[i] = get_reward_prediction(q_t, i)

                a = np.argmax(actions)

        # env.render()
        qa = np.concatenate((q_t, table[a]), axis=0)

        s, r, episode_complete, data = env.step(a)
        episode_reward += r

        if step_number is 0:
            g_x[0] = qa
            g_y[0] = np.array([r])
            mem_x[0] = qa
            mem_y[0] = np.array([r])

        g_x = np.vstack((g_x, qa))
        g_y = np.vstack((g_y, np.array([r])))

        if episode_complete:
            for i in range(0, g_y.shape[0]):
                if i is 0:
                    g_y[(g_y.shape[0] - 1) - i][0] = g_y[(g_y.shape[0] - 1) - i][0]
                else:
                    g_y[(g_y.shape[0] - 1) - i][0] = g_y[(g_y.shape[0] - 1) - i][0] + gamma * g_y[(g_y.shape[0] - 1) - i + 1][0]

            if mem_x.shape[0] is 1:
                mem_x = g_x
                mem_y = g_y
            else:
                mem_x = np.concatenate((mem_x, g_x), axis=0)
                mem_y = np.concatenate((mem_y, g_y), axis=0)

            if np.alen(mem_x) >= mem_max:
                for l in range(np.alen(g_x)):
                    mem_x = np.delete(mem_x, 0, axis=0)
                    mem_y = np.delete(mem_y, 0, axis=0)

        q_t = s

        if episode_complete and episode >= obs:
            if episode%10 == 0:
                model.fit(mem_x, mem_y, batch_size=32, epochs=epochs, verbose=0)

        if episode_complete:
            results.append(episode_reward)
            break

我正在运行数万集，但我的模型仍然不会收敛。它将开始减少约 5000 集以上的平均策略变化，同时增加平均奖励，但随后它会偏离深度，之后每集的平均奖励实际上会下降。我试过弄乱超参数，但我没有得到任何结果。我试图在DeepMind DQN 论文之后对我的代码进行建模。

标签： neural-networkkerasdeep-learningreinforcement-learningq-learning

解决方案

您可能希望将get_random_action函数更改为每集衰减 epsilon。毕竟，假设您的代理可以学习最佳策略，在某些时候您根本不想采取随机行动，对吧？这是一个稍微不同的版本get_random_action，可以为你做到这一点：

def get_random_action(epsilon, total_episodes, episode):
        explore_prob = epsilon - (epsilon * (episode / total_episodes))
        return np.random.rand(1) < explore_prob

在你的函数的这个修改版本中，epsilon 会随着每一集而略微减少。这可能有助于您的模型收敛。

有几种衰减参数的方法。有关更多信息，请查看此 Wikipedia 文章。

推荐阅读