PPO实战

哈哈初学，复现龙龙老师的实例！

state：是平衡小车上的杆子，观测状态由 4 个连续的参数组成：推车位置 [-2.4，2.4]，车速 [-∞，∞]，杆子角度 [～-41.8°，～41.8°] 与杆子末端速度 [-∞，∞]。

游戏结束：当极点与垂直方向的夹角超过15度时，或者推车从中心移出2.4个单位以上
向推车施加+1或-1的力来控制系统
杆保持直立的每个时间步长都提供+1的奖励

代码分析

经验池缓存

批训练条件：

Transition = namedtuple('Transition', ['state', 'action', 'a_log_prob', 'reward', 'next_state'])
trans = Transition(state, action, action_prob, reward, next_state)
agent.store_transition(trans)
self.buffer.append(transition)

            if done: # 合适的时间点训练网络（回合结束done=True才训练）
                if len(agent.buffer) >= batch_size:
                    agent.optimize() # 训练网络
                break

数据迭代次数:round()函数不设四舍五入位数，默认计算到整数。

 # 对缓冲池数据大致迭代10遍
        for _ in range(round(10*len(self.buffer)/batch_size)):

奖励计算

 Rs = []
        for r in reward[::-1]:
            R = r + gamma * R
            Rs.insert(0, R)  #在List 列表前添加新的R
        Rs = tf.constant(Rs, dtype=tf.float32) #将List转换为Tensor格式

重要性采样：old_action_log_prob为Actor策略网络得出的已经生成的buffer动作概率（策略采样offline）;pi_a为index对应的（历史）buffer中的状态再进行Actor网络在线得出的动作概率（目标策略online）

ratio = (pi_a / tf.gather(old_action_log_prob, index, axis=0))

PPO2误差：

ratio = (pi_a / tf.gather(old_action_log_prob, index, axis=0))
surr1 = ratio * advantage
surr2 = tf.clip_by_value(ratio, 1 - epsilon, 1 + epsilon) * advantage
# PPO误差函数
policy_loss = -tf.reduce_mean(tf.minimum(surr1, surr2))

函数备注：

tf.reshape(tensor,[-1,1])将张量变为一维列向量
tf.reshape(tensor,[1,-1])将张量变为一维行向量

tf.gather 可以根据索引号收集数据的目的。
如：考虑班级成绩册的例子,共有 4 个班
级,每个班级 35 个学生,8 门科目,保存成绩册的张量 shape 为[4,35,8]。例如在班级的维度上面我们可以像下面这样收集1和2班级的成绩册。

x = tf.random.uniform([4,35,8],maxval=100,dtype=tf.int32)
y=tf.gather(x,[0,1],axis=0)

buffer.append(transition) 在列表后面添加存储数据。

range()返回从0到4的5个数构成的list，而arange()返回一个array对象。不过他们的元素都是一样的。

Transition = namedtuple('Transition', ['state', 'action', 'a_log_prob', 'reward', 'next_state'])

下面的函数的作用为从数组x中取出n个元素，false表示每个元素不一样

**np.random.choice**(x,n,replace=false)

np.arange(len(self.buffer)) ：生成长度的数组，这里为0~39,40个数（buffer的数目），后面会用这些抽取的随机数作为训练样本的索引下标。

中间prob概率截图

a = tf.random.categorical(tf.math.log(prob), 1)[0]

变量监视：

PPO实战代码

import  matplotlib
from matplotlib import pyplot as plt
matplotlib.rcParams['font.size'] = 18
matplotlib.rcParams['figure.titlesize'] = 18
matplotlib.rcParams['figure.figsize'] = [9, 7]
matplotlib.rcParams['font.family'] = ['Microsoft YaHei']
matplotlib.rcParams['axes.unicode_minus']=False

plt.figure()

import  gym,os
import  numpy as np
import  tensorflow as tf
from    tensorflow import keras
from    tensorflow.keras import layers,optimizers,losses
from    collections import namedtuple
#from    torch.utils.data import SubsetRandomSampler,BatchSampler

env = gym.make('CartPole-v1')  # 创建游戏环境
env.seed(2222)
tf.random.set_seed(2222)
np.random.seed(2222)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
assert tf.__version__.startswith('2.')



gamma = 0.98 # 激励衰减因子
epsilon = 0.2 # PPO误差超参数0.8~1.2
batch_size = 32 # batch size


# 创建游戏环境
env = gym.make('CartPole-v0').unwrapped
Transition = namedtuple('Transition', ['state', 'action', 'a_log_prob', 'reward', 'next_state'])


class Actor(keras.Model):
    def __init__(self):
        super(Actor, self).__init__()
        # 策略网络，也叫Actor网络，输出为概率分布pi(a|s)
        self.fc1 = layers.Dense(100, kernel_initializer='he_normal')
        self.fc2 = layers.Dense(2, kernel_initializer='he_normal')

    def call(self, inputs):
        x = tf.nn.relu(self.fc1(inputs))
        x = self.fc2(x)
        x = tf.nn.softmax(x, axis=1) # 转换成概率
        return x

class Critic(keras.Model):
    def __init__(self):
        super(Critic, self).__init__()
        # 偏置b的估值网络，也叫Critic网络，输出为v(s)
        self.fc1 = layers.Dense(100, kernel_initializer='he_normal')
        self.fc2 = layers.Dense(1, kernel_initializer='he_normal')

    def call(self, inputs):
        x = tf.nn.relu(self.fc1(inputs))
        x = self.fc2(x)
        return x




class PPO():
    # PPO算法主体
    def __init__(self):
        super(Actor, self).__init__()
        self.actor = Actor() # 创建Actor网络
        self.critic = Critic() # 创建Critic网络
        self.buffer = [] # 数据缓冲池
        self.actor_optimizer = optimizers.Adam(1e-3) # Actor优化器
        self.critic_optimizer = optimizers.Adam(3e-3) # Critic优化器

    def select_action(self, s):
        # 送入状态向量，获取策略: [4]
        s = tf.constant(s, dtype=tf.float32)
        # s: [4] => [1,4]
        s = tf.expand_dims(s, axis=0)
        # 获取策略分布: [1, 2]
        prob = self.actor(s)
        # 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
        return a, float(prob[0][a]) # 返回动作及其概率

    def get_value(self, s):
        # 送入状态向量，获取策略: [4]
        s = tf.constant(s, dtype=tf.float32)
        # s: [4] => [1,4]
        s = tf.expand_dims(s, axis=0)
        # 获取策略分布: [1, 2]
        v = self.critic(s)[0]
        return float(v) # 返回v(s)

    def store_transition(self, transition):
        # 存储采样数据
        self.buffer.append(transition)

    def optimize(self):
        # 优化网络主函数
        # 从缓存中取出样本数据，转换成Tensor
        state = tf.constant([t.state for t in self.buffer], dtype=tf.float32)
        action = tf.constant([t.action for t in self.buffer], dtype=tf.int32)
        action = tf.reshape(action,[-1,1])
        reward = [t.reward for t in self.buffer]
        old_action_log_prob = tf.constant([t.a_log_prob for t in self.buffer], dtype=tf.float32)
        old_action_log_prob = tf.reshape(old_action_log_prob, [-1,1])
        # 通过MC方法循环计算R(st)
        R = 0
        Rs = []
        for r in reward[::-1]:
            R = r + gamma * R
            Rs.insert(0, R)
        Rs = tf.constant(Rs, dtype=tf.float32)
        # 对缓冲池数据大致迭代10遍
        for _ in range(round(10*len(self.buffer)/batch_size)):
            # 随机从缓冲池采样batch size大小样本
            index = np.random.choice(np.arange(len(self.buffer)), batch_size, replace=False)
            # 构建梯度跟踪环境
            with tf.GradientTape() as tape1, tf.GradientTape() as tape2:
                # 取出R(st)，[b,1]
                v_target = tf.expand_dims(tf.gather(Rs, index, axis=0), axis=1)
                # 计算v(s)预测值，也就是偏置b，我们后面会介绍为什么写成v
                v = self.critic(tf.gather(state, index, axis=0))
                delta = v_target - v # 计算优势值
                advantage = tf.stop_gradient(delta) # 断开梯度连接
                # 由于TF的gather_nd与pytorch的gather功能不一样，需要构造
                # gather_nd需要的坐标参数，indices:[b, 2]
                # pi_a = pi.gather(1, a) # pytorch只需要一行即可实现
                a = tf.gather(action, index, axis=0) # 取出batch的动作at
                # batch的动作分布pi(a|st)
                pi = self.actor(tf.gather(state, index, axis=0))
                indices = tf.expand_dims(tf.range(a.shape[0]), axis=1)
                indices = tf.concat([indices, a], axis=1)
                pi_a = tf.gather_nd(pi, indices)  # 动作的概率值pi(at|st), [b]
                pi_a = tf.expand_dims(pi_a, axis=1)  # [b]=> [b,1]
                # 重要性采样
                ratio = (pi_a / tf.gather(old_action_log_prob, index, axis=0))
                surr1 = ratio * advantage
                surr2 = tf.clip_by_value(ratio, 1 - epsilon, 1 + epsilon) * advantage
                # PPO误差函数
                policy_loss = -tf.reduce_mean(tf.minimum(surr1, surr2))
                # 对于偏置v来说，希望与MC估计的R(st)越接近越好
                value_loss = losses.MSE(v_target, v)
            # 优化策略网络
            grads = tape1.gradient(policy_loss, self.actor.trainable_variables)
            self.actor_optimizer.apply_gradients(zip(grads, self.actor.trainable_variables))
            # 优化偏置值网络
            grads = tape2.gradient(value_loss, self.critic.trainable_variables)
            self.critic_optimizer.apply_gradients(zip(grads, self.critic.trainable_variables))

        self.buffer = []  # 清空已训练数据


def main():
    agent = PPO()
    returns = [] # 统计总回报
    total = 0 # 一段时间内平均回报
    for i_epoch in range(1000): # 训练回合数
        state = env.reset() # 复位环境
        for t in range(500): # 最多考虑500步
            # 通过最新策略与环境交互
            action, action_prob = agent.select_action(state)
            next_state, reward, done, _ = env.step(action)
            # 构建样本并存储
            trans = Transition(state, action, action_prob, reward, next_state)
            agent.store_transition(trans)
            state = next_state # 刷新状态
            total += reward # 累积激励
            env.render()
            if done: # 合适的时间点训练网络
                if len(agent.buffer) >= batch_size:
                    agent.optimize() # 训练网络
                break

        if i_epoch % 20 == 0: # 每20个回合统计一次平均回报
            returns.append(total/20)
            total = 0
            print(i_epoch, returns[-1])

    print(np.array(returns))
    plt.figure()
    plt.plot(np.arange(len(returns))*20, np.array(returns))
    plt.plot(np.arange(len(returns))*20, np.array(returns), 's')
    plt.xlabel('回合数')
    plt.ylabel('总回报')
    plt.savefig('ppo-tf-cartpole.svg')


if __name__ == '__main__':
    main()
    print("end")

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