Deep-Learning-with-TensorFlow-book/ch14-强化学习/REINFORCE_tf.py

import 	gym,os
import  numpy as np
import  matplotlib
from 	matplotlib import pyplot as plt
# Default parameters for plots
matplotlib.rcParams['font.size'] = 18
matplotlib.rcParams['figure.titlesize'] = 18
matplotlib.rcParams['figure.figsize'] = [9, 7]
matplotlib.rcParams['font.family'] = ['KaiTi']
matplotlib.rcParams['axes.unicode_minus']=False 

import 	tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import layers,optimizers,losses
from    PIL import Image
env = gym.make('CartPole-v1')  # 创建游戏环境
env.seed(2333)
tf.random.set_seed(2333)
np.random.seed(2333)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')

learning_rate = 0.0002
gamma         = 0.98

class Policy(keras.Model):
    # 策略网络，生成动作的概率分布
    def __init__(self):
        super(Policy, self).__init__()
        self.data = [] # 存储轨迹
        # 输入为长度为4的向量，输出为左、右2个动作
        self.fc1 = layers.Dense(128, kernel_initializer='he_normal')
        self.fc2 = layers.Dense(2, kernel_initializer='he_normal')
        # 网络优化器
        self.optimizer = optimizers.Adam(lr=learning_rate)

    def call(self, inputs, training=None):
        # 状态输入s的shape为向量：[4]
        x = tf.nn.relu(self.fc1(inputs))
        x = tf.nn.softmax(self.fc2(x), axis=1)
        return x

    def put_data(self, item):
        # 记录r,log_P(a|s)
        self.data.append(item)

    def train_net(self, tape):
        # 计算梯度并更新策略网络参数。tape为梯度记录器
        R = 0 # 终结状态的初始回报为0
        for r, log_prob in self.data[::-1]:#逆序取
            R = r + gamma * R # 计算每个时间戳上的回报
            # 每个时间戳都计算一次梯度
            # grad_R=-log_P*R*grad_theta
            loss = -log_prob * R
            with tape.stop_recording():
                # 优化策略网络
                grads = tape.gradient(loss, self.trainable_variables)
                # print(grads)
                self.optimizer.apply_gradients(zip(grads, self.trainable_variables))
        self.data = [] # 清空轨迹

def main():
    pi = Policy() # 创建策略网络
    pi(tf.random.normal((4,4)))
    pi.summary()
    score = 0.0 # 计分
    print_interval = 20 # 打印间隔
    returns = []

    for n_epi in range(400):
        s = env.reset() # 回到游戏初始状态，返回s0
        with tf.GradientTape(persistent=True) as tape:
            for t in range(501): # CartPole-v1 forced to terminates at 500 step.
                # 送入状态向量，获取策略
                s = tf.constant(s,dtype=tf.float32)
                # s: [4] => [1,4]
                s = tf.expand_dims(s, axis=0)
                prob = pi(s) # 动作分布:[1,2]
                # 从类别分布中采样1个动作, shape: [1]
                a = tf.random.categorical(tf.math.log(prob), 1)[0]
                a = int(a) # Tensor转数字
                s_prime, r, done, info = env.step(a)
                # 记录动作a和动作产生的奖励r
                # prob shape:[1,2]
                pi.put_data((r, tf.math.log(prob[0][a])))
                s = s_prime # 刷新状态
                score += r # 累积奖励

                if n_epi >1000:
                    env.render()
                    # im = Image.fromarray(s)
                    # im.save("res/%d.jpg" % info['frames'][0])

                if done:  # 当前episode终止
                    break
            # episode终止后，训练一次网络
            pi.train_net(tape)
        del tape

        if n_epi%print_interval==0 and n_epi!=0:
            returns.append(score/print_interval)
            print(f"# of episode :{n_epi}, avg score : {score/print_interval}")
            score = 0.0
    env.close() # 关闭环境

    plt.plot(np.arange(len(returns))*print_interval, returns)
    plt.plot(np.arange(len(returns))*print_interval, returns, 's')
    plt.xlabel('回合数')
    plt.ylabel('总回报')
    plt.savefig('reinforce-tf-cartpole.svg')

if __name__ == '__main__':
    main()