强化学习从基础到进阶-案例与实践[5.1]：Policy Gradient-Cart pole游戏展示

强化学习（Reinforcement learning，简称RL）是机器学习中的一个领域，区别与监督学习和无监督学习，强调如何基于环境而行动，以取得最大化的预期利益。
基本操作步骤：智能体agent在环境environment中学习，根据环境的状态state（或观测到的observation），执行动作action，并根据环境的反馈reward（奖励）来指导更好的动作。

比如本项目的Cart pole小游戏中，agent就是动图中的杆子，杆子有向左向右两种action。

1.Policy Gradient简介

在强化学习中，有两大类方法，一种基于值（Value-based），一种基于策略（Policy-based）
- Value-based的算法的典型代表为Q-learning和SARSA，将Q函数优化到最优，再根据Q函数取最优策略。
- Policy-based的算法的典型代表为Policy Gradient，直接优化策略函数。
采用神经网络拟合策略函数，需计算策略梯度用于优化策略网络。
- 优化的目标是在策略π(s,a)的期望回报：所有的轨迹获得的回报R与对应的轨迹发生概率p的加权和，当N足够大时，可通过采样N个Episode求平均的方式近似表达。
- 优化目标对参数θ求导后得到策略梯度：

## 安装依赖
!pip install pygame
!pip install gym
!pip install atari_py
!pip install parl

import gym
import os
import random
import collectionsimport paddle
import paddle.nn as nn
import numpy as np
import paddle.nn.functional as F

2.模型Model

这里的模型可以根据自己的需求选择不同的神经网络组建。

PolicyGradient用来定义前向(Forward)网络，可以自由的定制自己的网络结构。

class PolicyGradient(nn.Layer):def __init__(self, act_dim):super(PolicyGradient, self).__init__()act_dim = act_dimhid1_size = act_dim * 10self.linear1 = nn.Linear(in_features=4, out_features=hid1_size)self.linear2 = nn.Linear(in_features=hid1_size, out_features=act_dim)def forward(self, obs):out = self.linear1(obs)out = paddle.tanh(out)out = self.linear2(out)out = F.softmax(out)return out

3.智能体Agent的学习函数

这里包括模型探索与模型训练两个部分

Agent负责算法与环境的交互，在交互过程中把生成的数据提供给Algorithm来更新模型(Model)，数据的预处理流程也一般定义在这里。

def sample(obs, MODEL):global ACTION_DIMobs = np.expand_dims(obs, axis=0)obs = paddle.to_tensor(obs, dtype='float32')act = MODEL(obs)act_prob = np.squeeze(act, axis=0)act = np.random.choice(range(ACTION_DIM), p=act_prob.numpy())return actdef learn(obs, action, reward, MODEL):obs = np.array(obs).astype('float32')obs = paddle.to_tensor(obs)act_prob = MODEL(obs)action = paddle.to_tensor(action.astype('int32'))log_prob = paddle.sum(-1.0 * paddle.log(act_prob) * F.one_hot(action, act_prob.shape[1]), axis=1)reward = paddle.to_tensor(reward.astype('float32'))cost = log_prob * rewardcost = paddle.sum(cost)opt = paddle.optimizer.Adam(learning_rate=LEARNING_RATE,parameters=MODEL.parameters())  # 优化器(动态图)cost.backward()opt.step()opt.clear_grad()return cost.numpy()

4.模型梯度更新算法

def run_train(env, MODEL):MODEL.train()obs_list, action_list, total_reward = [], [], []obs = env.reset()while True:# 获取随机动作和执行游戏obs_list.append(obs)action = sample(obs, MODEL) # 采样动作action_list.append(action)obs, reward, isOver, info = env.step(action)total_reward.append(reward)# 结束游戏if isOver:breakreturn obs_list, action_list, total_rewarddef evaluate(model, env, render=False):model.eval()eval_reward = []for i in range(5):obs = env.reset()episode_reward = 0while True:obs = np.expand_dims(obs, axis=0)obs = paddle.to_tensor(obs, dtype='float32')action = model(obs)action = np.argmax(action.numpy())obs, reward, done, _ = env.step(action)episode_reward += rewardif render:env.render()if done:breakeval_reward.append(episode_reward)return np.mean(eval_reward)

5.训练函数与验证函数

设置超参数

LEARNING_RATE = 0.001  # 学习率大小OBS_DIM = None
ACTION_DIM = None# 根据一个episode的每个step的reward列表，计算每一个Step的Gt
def calc_reward_to_go(reward_list, gamma=1.0):for i in range(len(reward_list) - 2, -1, -1):# G_t = r_t + γ·r_t+1 + ... = r_t + γ·G_t+1reward_list[i] += gamma * reward_list[i + 1]  # Gtreturn np.array(reward_list)def main():global OBS_DIMglobal ACTION_DIMtrain_step_list = []train_reward_list = []evaluate_step_list = []evaluate_reward_list = []# 初始化游戏env = gym.make('CartPole-v0')# 图像输入形状和动作维度action_dim = env.action_space.nobs_dim = env.observation_space.shape[0]OBS_DIM = obs_dimACTION_DIM = action_dimmax_score = -int(1e4)# 创建存储执行游戏的内存MODEL = PolicyGradient(ACTION_DIM)TARGET_MODEL = PolicyGradient(ACTION_DIM)# 开始训练print("start training...")# 训练max_episode个回合，test部分不计算入episode数量for i in range(1000):obs_list, action_list, reward_list = run_train(env, MODEL)if i % 10 == 0:print("Episode {}, Reward Sum {}.".format(i, sum(reward_list)))batch_obs = np.array(obs_list)batch_action = np.array(action_list)batch_reward = calc_reward_to_go(reward_list)cost = learn(batch_obs, batch_action, batch_reward, MODEL)if (i + 1) % 100 == 0:total_reward = evaluate(MODEL, env, render=False) # render=True 查看渲染效果，需要在本地运行，AIStudio无法显示print("Test reward: {}".format(total_reward))if __name__ == '__main__':main()

W0630 11:26:18.969960   322 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 11.2
W0630 11:26:18.974581   322 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.start training...
Episode 0, Reward Sum 37.0.
Episode 10, Reward Sum 27.0.
Episode 20, Reward Sum 32.0.
Episode 30, Reward Sum 20.0.
Episode 40, Reward Sum 18.0.
Episode 50, Reward Sum 38.0.
Episode 60, Reward Sum 52.0.
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Test reward: 42.8
Episode 100, Reward Sum 28.0.
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Episode 120, Reward Sum 30.0.
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Test reward: 42.8
Episode 200, Reward Sum 23.0.
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Episode 230, Reward Sum 59.0.
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Episode 290, Reward Sum 64.0.
Test reward: 116.8
Episode 300, Reward Sum 54.0.
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Episode 340, Reward Sum 89.0.
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Test reward: 94.0
Episode 400, Reward Sum 70.0.
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Test reward: 57.6
Episode 500, Reward Sum 40.0.
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Test reward: 147.2
Episode 600, Reward Sum 24.0.
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Test reward: 84.2
Episode 700, Reward Sum 34.0.
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Episode 730, Reward Sum 72.0.
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Episode 790, Reward Sum 147.0.
Test reward: 123.0
Episode 800, Reward Sum 129.0.
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Episode 830, Reward Sum 54.0.
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Test reward: 104.8
Episode 900, Reward Sum 41.0.
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Episode 950, Reward Sum 114.0.
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Episode 980, Reward Sum 94.0.
Episode 990, Reward Sum 90.0.
Test reward: 72.2