生成一个gym环境的Policy gradient，要求画出loss函数

由于Policy gradient的loss函数通常是非常复杂的，取决于具体的环境和策略。因此，这里提供一个简单的CartPole-v0环境的Policy gradient的例子，并画出其loss函数。

import gym
import numpy as np
import tensorflow as tf

# 创建CartPole环境
env = gym.make('CartPole-v0')

# 定义超参数
learning_rate = 0.01
gamma = 0.99
num_episodes = 1000

# 定义神经网络
inputs = tf.placeholder(shape=[None, 4], dtype=tf.float32)
W1 = tf.Variable(tf.random_normal([4, 16]))
b1 = tf.Variable(tf.zeros([16]))
hidden = tf.nn.relu(tf.matmul(inputs, W1) + b1)
W2 = tf.Variable(tf.random_normal([16, 2]))
b2 = tf.Variable(tf.zeros([2]))
outputs = tf.nn.softmax(tf.matmul(hidden, W2) + b2)

# 定义损失函数和优化器
actions = tf.placeholder(shape=[None], dtype=tf.int32)
rewards = tf.placeholder(shape=[None], dtype=tf.float32)
indices = tf.range(0, tf.shape(outputs)[0]) * tf.shape(outputs)[1] + actions
act_prob = tf.gather(tf.reshape(outputs, [-1]), indices)
loss = -tf.reduce_mean(tf.log(act_prob) * rewards)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss)

# 开始训练
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for episode in range(num_episodes):
obs = env.reset()
observations = []
actions_taken = []
rewards_received = []
done = False
while not done:
# 选择动作并执行
action_prob = sess.run(outputs, feed_dict={inputs: [obs]})[0]
action = np.random.choice(np.arange(2), p=action_prob)
new_obs, reward, done, info = env.step(action)
# 保存观测值、动作和奖励
observations.append(obs)
actions_taken.append(action)
rewards_received.append(reward)
obs = new_obs
# 计算回报
returns = np.zeros_like(rewards_received)
G = 0
for t in reversed(range(len(rewards_received))):
G = gamma * G + rewards_received[t]
returns[t] = G
# 归一化回报
returns -= np.mean(returns)
returns /= np.std(returns)
# 计算损失并更新策略网络
_, l = sess.run([optimizer, loss], feed_dict={
inputs: np.array(observations),
actions: np.array(actions_taken),
rewards: returns})
print('Episode %d, loss: %f' % (episode + 1, l))

# 关闭环境和会话
env.close()
sess.close()

# 画出loss函数
import matplotlib.pyplot as plt

x = np.arange(num_episodes)
y = np.zeros(num_episodes)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for episode in range(num_episodes):
obs = env.reset()
observations = []
actions_taken = []
rewards_received = []
done = False
while not done:
action_prob = sess.run(outputs, feed_dict={inputs: [obs]})[0]
action = np.random.choice(np.arange(2), p=action_prob)
new_obs, reward, done, info = env.step(action)
observations.append(obs)
actions_taken.append(action)
rewards_received.append(reward)
obs = new_obs
returns = np.zeros_like(rewards_received)
G = 0
for t in reversed(range(len(rewards_received))):
G = gamma * G + rewards_received[t]
returns[t] = G
returns -= np.mean(returns)
returns /= np.std(returns)
_, l = sess.run([optimizer, loss], feed_dict={
inputs: np.array(observations),
actions: np.array(actions_taken),
rewards: returns})
y[episode] = l
plt.plot(x, y)
plt.xlabel('Episode')
plt.ylabel('Loss')
plt.show()