【零基础强化学习】100行代码教你实现基于DQN的gym登山车

更多代码: gitee主页：https://gitee.com/GZHzzz
博客主页： CSDN：https://blog.csdn.net/gzhzzaa

写在前面

作为一个新手，写这个强化学习-基础知识专栏是想和大家分享一下自己强化学习的学习历程，希望大家互相交流一起进步。希望自己在2022年能保证把强化学习基础概念都过一遍，主要是成体系介绍强化学习的基础知识，而且在gitee收集了强化学习经典论文和基于pytorch的经典模型 ，大家一起互相学习啊！可能会有很多错漏，希望大家批评指正！不要高估一年的努力，也不要低估十年的积累，与君共勉！

show me code, no bb

#这是一堆初始化
import gym
import random
import torch
import torch.nn as nn
from torch.utils.data import Dataset
import os
#env = gym.make('CartPole-v0')
env = gym.make('MountainCar-v0') #action = (0,1,2) = (left, no_act, right)
#env = gym.make('Hopper-v3')
print(env.observation_space)
#print(env.action_space)
#简单的线性模型
def mkdir(path):
    folder = os.path.exists(path)
    if not folder:                   
        os.makedirs(path)
def GetModel():
    #In features:2(state) ,out:3 action q
    return nn.Sequential(nn.Linear(2, 16), 
                         nn.LeakyReLU(inplace=True), 
                         nn.Linear(16,24),
                         nn.LeakyReLU(inplace=True), 

                         nn.Linear(24,3))
#创建数据集
class RLDataset(Dataset):
    def __init__(self, samples, transform = None, target_transform = None):
        #samples = [(s,a,r,s_), ...]
        self.samples = self.transform(samples)
    def __getitem__(self, index):
        #if self.transform is not None:
        #    img = self.transform(img) 
        return self.samples[index]
    def __len__(self):
        return len(self.samples)
    def transform(self, samples):
        transSamples = []
        for (s,a,r,s_) in samples:
            sT = torch.tensor(s,).to(torch.float32)
            sT_ = torch.tensor(s_).to(torch.float32)
            transSamples.append((sT, a, r, sT_))
        return transSamples

#采样环境函数，可以设置随机操作的概率。重点在于reward的设计
def GetSamplesFromEnv(env, model, epoch, max_steps, drop_ratio = 0.8):
    train_samples = []
    each_sample = None
    env.reset()
    observation_new = None
    observation_old = None
    model.eval()
    for i_episode in range(epoch):
        observation_new = env.reset()
        observation_old = env.reset()
        for t in range(max_steps):
            env.render()
            #print(observation)
            if random.random() > 1-drop_ratio:
                action = env.action_space.sample()
            else:
                inputT = torch.tensor(observation_new).to(torch.float32)
                action = torch.argmax(model(inputT)).item()
                #print(action)
            observation_new, reward, done, info = env.step(action)
            #print(reward)
            #We record samples.
            if t > 0 :
                #reward += observation_new[0]
                #if observation_new[0] > -0.35:
                #    reward += (observation_new[0] + 0.36)*5
                if observation_new[0] > -0.2:
                    reward += 0.2
                elif observation_new[0] > -0.15:
                    reward += 0.5
                elif observation_new[0] > -0.1:
                    reward += 0.7
                each_sample = (observation_old, action, reward, observation_new)
                train_samples.append(each_sample)
            observation_old = observation_new
            if done:
            	#失败的采样不打印出来
                if t != 199:
                    print("Episode finished after {} timesteps".format(t+1))
                break
    return train_samples
#训练网络。这里可能gather函数比较绕，还有双网络更新比较费解。忽略掉这些，和正常训练循环一样
#gamma是贝尔曼方程里的衰减因子
def TrainNet(net_target, net_eval, trainloader, criterion, optimizer, device, epoch_total, gamma):
    running_loss = 0.0
    iter_times = 0
    net_target.eval()
    net_eval.train()
    for epoch in range(epoch_total + 1):
        if epoch > 0:           
            print('epoch %d, loss %.5f' % (epoch, running_loss))
        running_loss = 0.0
        if epoch == epoch_total: 
            break        
        for i, data in enumerate(trainloader, 0):
            if iter_times % 100 == 0:
                net_target.load_state_dict(net_eval.state_dict())
            s,a,r,s_ = data
            optimizer.zero_grad()
            #output = Q_predicted.
            q_t0 = net_eval(s)
            q_t1 = net_target(s_).detach()
            q_t1 = gamma * (r + torch.max(q_t1,dim=1)[0]).to(torch.float32)
        
            loss = criterion(q_t1, torch.gather(q_t0, dim=1, index=a.unsqueeze(1)).squeeze(1))
            loss.sum().backward()
            optimizer.step()
            running_loss += loss.item()
            iter_times += 1
    net_target.load_state_dict(net_eval.state_dict())    
    print('Finished Training')
if __name__ == '__main__':
    
    mkdir('model')
    
    #最后是一大堆主循环
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    net_target, net_eval = GetModel(), GetModel()
    criterion = nn.MSELoss()
    optimizer = torch.optim.Adam(net_eval.parameters(),lr=0.01)
    train_samples = []
    goodmodel_idx = 0
    for i in range(300):
        drop_ratio = 0.8 - 0.0077*i
        sample_times = 10
        tmpSample = GetSamplesFromEnv(env,net_eval, sample_times, 200, drop_ratio)
        train_samples += tmpSample
        #每次sample的长度就代表了采取的步数，登山车里是越小越好。如果是倒立摆，则是越大越好
        if len(tmpSample) < sample_times * 160:
            print("good model!save it!")
            torch.save(net_eval.state_dict(), "goodmodel" + str(goodmodel_idx) + ".pth")
            goodmodel_idx += 1
        #dataset里存着最新的不超过4000的样本
        if len(train_samples) > 4000:
            train_samples = train_samples[len(tmpSample):len(train_samples)]
        trainset = RLDataset(train_samples)
        trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True, num_workers=0,pin_memory=True)
        TrainNet(net_target, net_eval, trainloader, criterion, optimizer, device, 10, 0.9)

        if i%50 == 0:
            PATH = "model/model"+str(i)+".pth"
            torch.save(net_eval.state_dict(), PATH)
    env.close()
    #这一堆是测试看效果用的
    #PATH = 'model/model42.pth'
    #net_eval.load_state_dict(torch.load(PATH))
    #net_target.load_state_dict(torch.load(PATH))
    #GetSamplesFromEnv(env,net_eval, 20, 200, 0)

• 自己过了一遍，代码可直接跑通，包括模型保存，模型测试，你懂的！

界面展示

写在最后

十年磨剑，与君共勉！
更多代码：gitee主页：https://gitee.com/GZHzzz
博客主页：CSDN：https://blog.csdn.net/gzhzzaa

• Fighting!

while True:
	Go life

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