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我现在尝试学习强化学习的概念。因此，我尝试使用 tensorflow 为推车杆示例实现 SARSA 算法。我将我的算法与对 q 值函数使用线性逼近函数的算法进行了比较，发现我的算法非常相似。不幸的是，我的实现似乎是错误的或效率低下的，因为学习成功是相当有限的。有没有人可以告诉我我是否做错了什么以及它是什么？我的实现代码是：

import numpy as np
import matplotlib.pylab as plt
import random
import gym


#define a neural network which returns two action dependent q-values given a state
neural_net = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation = 'relu', input_shape = [4]),
    tf.keras.layers.Dense(2)
])

#return the neural network's q-value for a specific action
def q_value(state, action):
    return neural_net(tf.convert_to_tensor([state]))[0, action]

#act either randomly or choose the action which maximizes the q-value
def policy(state, epsilon):
    values = neural_net(tf.convert_to_tensor([state]))
    if np.random.rand() < epsilon:
        return random.choice([0, 1])
    else:
        return np.argmax(values)

#intialize gym environment
env = gym.make('CartPole-v0')

#hyperparameters
discount = 0.99
optimizer = tf.keras.optimizers.Adam()
episodes = 1000
epsilon = 0.30

#collect reward for each episode
rewards = []
for episode in range(episodes):

    #start trajectory for episode
    state = env.reset()

    #record rewards during episode
    sum_returns = 0

    #decrease random action after the first 100 episodes
    if episode == 100:
        epsilon = 0.10

    #Q-learning
    while True:
        action = policy(state, epsilon)
        next_state, reward, done, _ = env.step(action)
        next_action = policy(next_state, epsilon)
        sum_returns += 1

        if done:
            with tf.GradientTape() as tape:
                tape.watch(neural_net.trainable_variables)
                q_hat = q_value(state, action)
                y = reward
                loss = tf.square(y - q_hat)

            gradients = tape.gradient(loss, neural_net.trainable_variables)
            optimizer.apply_gradients(zip(gradients, neural_net.trainable_variables))
            break
        else:
            with tf.GradientTape() as tape:
                tape.watch(neural_net.trainable_variables)
                q_hat = q_value(state, action)
                y = reward + discount * q_value(next_state, next_action)
                loss = tf.square(y - q_hat)

            gradients = tape.gradient(loss, neural_net.trainable_variables)
            optimizer.apply_gradients(zip(gradients, neural_net.trainable_variables))
            state = next_state

    rewards.append(sum_returns)

#plot learning over time
plt.plot([episode for episode in range(episodes)], rewards)
plt.show()```
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1 回答 1

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我快速查看了您的代码，似乎神经网络无法知道 Q 值的新估计y与选择的操作有关a，因为您通过了相同的状态，而不管之后做出的选择.

我的建议是将状态连接两次 - 因为您有两个操作 - 当您选择第一个操作时，您只需将当前状态一次添加到该中间表示的前半部分，而将向量的后半部分留空。在选择第二个动作时，您会做类似的事情，但您会将向量的前半部分留空。

查看我在实施 SARSA 时发现的 Coursera 上的此链接：https ://www.coursera.org/lecture/prediction-control-function-approximation/episodic-sarsa-with-function-approximation-z9xQJ

于 2021-06-04T05:16:58.287 回答