如何入门强化学习

• 前言
• 一、强化学习学习之路
• 二、开源代码DDPG详解
• 总结

前言

很多同学在入门强化学习的时候都会遇到困难，那我这里就简单介绍一下应该如何入门强化学习，并以开源代码为例详解强化学习实战。

一、强化学习学习之路

这边首先推荐莫凡python，人工智能的初学者如果不知道莫凡，那可真是纵称英雄也枉然。
他的官网长这样，链接在此！
链接: https://mofanpy.com/.

莫凡python的内容以简单著称，每个视频只有短短几分钟，长一点的也就20分钟左右，能够迅速的帮你入门强化学习，但是仅仅看这个是不够的，还需要扎实的理论基础，那就需要转战B站。

Bilibili有UCL大学的David Silver教授教学的Reinforcement learning课程。
链接在此！链接: https://www.bilibili.com/video/BV1kb411i7KG?from=search&seid=15348457829154001765&spm_id_from=333.337.0.0.
David Silver教授推导出了DPG公式，非常厉害的一个人物，所以他的课程必听！
这个课程主要以公式推导为主，能够让初学者对强化学习的基础公式推导有一定的了解，例如贝尔曼公式。

当然B站李宏毅老师的强化学习课程也非常值得一听，但是我个人觉得他和莫凡python的课程差不多，但是他没有代码层面的教学，他的课程更多是在理论推导和科普介绍…
最后最后，我要推荐一本书籍，这本书籍也非常适用于初学者，Sutton的书很厚，我个人觉得不适用于初学者。
清华大学出版社邹伟老师出品的强化学习，结合了很多莫凡Python的代码，能够很好的将理论与实际相结合，很多代码都有注释，能够迅速帮初学者入门。

二、开源代码DDPG详解

如果说到强化学习，那么必须要会的就是Python和Gym
代码从Github上搜索DDPG就能够获取，这里采用的是floodsung的项目。

关键的几个文件：

actor_network
critic.network
ddpg//用于将各个模块连在一起
gym_ddpg//主文件，用于将DDPG与环境互动
ou_noise//Ou噪声
replay_buffer//经验存储

代码如下（示例）：

//ou_noise
import numpy as np
import numpy.random as nr

class OUNoise:
    """docstring for OUNoise"""
    def __init__(self,action_dimension,mu=0, theta=0.15, sigma=0.2):
        self.action_dimension = action_dimension
        self.mu = mu
        self.theta = theta
        self.sigma = sigma
        self.state = np.ones(self.action_dimension) * self.mu
        self.reset()

    def reset(self):
        self.state = np.ones(self.action_dimension) * self.mu

    def noise(self): //按照公式建立OU噪声，更符合时间序列的样本
        x = self.state
        dx = self.theta * (self.mu - x) + self.sigma * nr.randn(len(x))
        self.state = x + dx
        return self.state
//以下内容用不到，只是看一看ou噪声是啥
if __name__ == '__main__':
    ou = OUNoise(3)
    states = []
    for i in range(1000):
        states.append(ou.noise())
    import matplotlib.pyplot as plt

    plt.plot(states)
    plt.show()

//replay_buffer
from collections import deque
import random

class ReplayBuffer(object):

    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.num_experiences = 0
        self.buffer = deque()

    def get_batch(self, batch_size)://随机批量获取，用于训练
        # Randomly sample batch_size examples
        return random.sample(self.buffer, batch_size)

    def size(self):
        return self.buffer_size
//将agent与环境互动的情况存储在buffer中，如果超过最大存储量，就删去最早的。
    def add(self, state, action, reward, new_state, done):
        experience = (state, action, reward, new_state, done)
        if self.num_experiences < self.buffer_size:
            self.buffer.append(experience)
            self.num_experiences += 1
        else:
            self.buffer.popleft()
            self.buffer.append(experience)
//计数，一般用不到
    def count(self):
        # if buffer is full, return buffer size
        # otherwise, return experience counter
        return self.num_experiences
//删除，一般用不到
    def erase(self):
        self.buffer = deque()
        self.num_experiences = 0

//actor_network
import tensorflow as tf 
import numpy as np
import math


# Hyper Parameters
LAYER1_SIZE = 400
LAYER2_SIZE = 300
LEARNING_RATE = 1e-4
TAU = 0.001
BATCH_SIZE = 64

class ActorNetwork:
	"""docstring for ActorNetwork"""
	def __init__(self,sess,state_dim,action_dim):

		self.sess = sess
		self.state_dim = state_dim
		self.action_dim = action_dim
		# create actor network
		self.state_input,self.action_output,self.net = self.create_network(state_dim,action_dim)
		
		# create target actor network
		self.target_state_input,self.target_action_output,self.target_update,self.target_net = self.create_target_network(state_dim,action_dim,self.net)

		# define training rules
		self.create_training_method()

		self.sess.run(tf.initialize_all_variables())

		self.update_target()
		#self.load_network()
//计算梯度，gradients函数必须要有参数是相关的才能求，如果是两个数值不能够使用这个函数
	def create_training_method(self):
		self.q_gradient_input = tf.placeholder("float",[None,self.action_dim])
		self.parameters_gradients = tf.gradients(self.action_output,self.net,-self.q_gradient_input)
		self.optimizer = tf.train.AdamOptimizer(LEARNING_RATE).apply_gradients(zip(self.parameters_gradients,self.net))
//建立actor网络，（state_dim,400),(400,300),(300,action_dim)建立了这样的线性网络。
	def create_network(self,state_dim,action_dim):
		layer1_size = LAYER1_SIZE
		layer2_size = LAYER2_SIZE

		state_input = tf.placeholder("float",[None,state_dim])

		W1 = self.variable([state_dim,layer1_size],state_dim)
		b1 = self.variable([layer1_size],state_dim)
		W2 = self.variable([layer1_size,layer2_size],layer1_size)
		b2 = self.variable([layer2_size],layer1_size)
		W3 = tf.Variable(tf.random_uniform([layer2_size,action_dim],-3e-3,3e-3))
		b3 = tf.Variable(tf.random_uniform([action_dim],-3e-3,3e-3))

		layer1 = tf.nn.relu(tf.matmul(state_input,W1) + b1)
		layer2 = tf.nn.relu(tf.matmul(layer1,W2) + b2)
		action_output = tf.tanh(tf.matmul(layer2,W3) + b3)

		return state_input,action_output,[W1,b1,W2,b2,W3,b3]
//建立目标网络，同上
	def create_target_network(self,state_dim,action_dim,net):
		state_input = tf.placeholder("float",[None,state_dim])
		ema = tf.train.ExponentialMovingAverage(decay=1-TAU)
		target_update = ema.apply(net)
		target_net = [ema.average(x) for x in net]

		layer1 = tf.nn.relu(tf.matmul(state_input,target_net[0]) + target_net[1])
		layer2 = tf.nn.relu(tf.matmul(layer1,target_net[2]) + target_net[3])
		action_output = tf.tanh(tf.matmul(layer2,target_net[4]) + target_net[5])

		return state_input,action_output,target_update,target_net
//更新
	def update_target(self):
		self.sess.run(self.target_update)
//训练，这个函数很重要
	def train(self,q_gradient_batch,state_batch):
		self.sess.run(self.optimizer,feed_dict={
			self.q_gradient_input:q_gradient_batch,
			self.state_input:state_batch
			})
//用于生成批量state的动作
	def actions(self,state_batch):
		return self.sess.run(self.action_output,feed_dict={
			self.state_input:state_batch
			})
//用于生成单个state的动作，例如与环境的互动中
	def action(self,state):
		return self.sess.run(self.action_output,feed_dict={
			self.state_input:[state]
			})[0]

//目标期望动作
	def target_actions(self,state_batch):
		return self.sess.run(self.target_action_output,feed_dict={
			self.target_state_input:state_batch
			})

	# f fan-in size
	def variable(self,shape,f):
		return tf.Variable(tf.random_uniform(shape,-1/math.sqrt(f),1/math.sqrt(f)))
'''
//agent训练到一定程度时存储网络，下次用的时候加载网络
	def load_network(self):
		self.saver = tf.train.Saver()
		checkpoint = tf.train.get_checkpoint_state("saved_actor_networks")
		if checkpoint and checkpoint.model_checkpoint_path:
			self.saver.restore(self.sess, checkpoint.model_checkpoint_path)
			print "Successfully loaded:", checkpoint.model_checkpoint_path
		else:
			print "Could not find old network weights"
	def save_network(self,time_step):
		print 'save actor-network...',time_step
		self.saver.save(self.sess, 'saved_actor_networks/' + 'actor-network', global_step = time_step)
'''

由于critic网络和actor网络在网络结构上是一样的，就不重复介绍了。

//ddpg
import gym
import tensorflow as tf
import numpy as np
from ou_noise import OUNoise
from critic_network import CriticNetwork 
from actor_network_bn import ActorNetwork //注意加载的头文件
from replay_buffer import ReplayBuffer

# Hyper Parameters:

REPLAY_BUFFER_SIZE = 1000000
REPLAY_START_SIZE = 10000
BATCH_SIZE = 64
GAMMA = 0.99


class DDPG:
    """docstring for DDPG"""
    def __init__(self, env):
        self.name = 'DDPG' # name for uploading results
        self.environment = env
        # Randomly initialize actor network and critic network
        # with both their target networks
        self.state_dim = env.observation_space.shape[0]
        self.action_dim = env.action_space.shape[0]

        self.sess = tf.InteractiveSession()

        self.actor_network = ActorNetwork(self.sess,self.state_dim,self.action_dim)
        self.critic_network = CriticNetwork(self.sess,self.state_dim,self.action_dim)
        
        # initialize replay buffer
        self.replay_buffer = ReplayBuffer(REPLAY_BUFFER_SIZE)

        # Initialize a random process the Ornstein-Uhlenbeck process for action exploration
        self.exploration_noise = OUNoise(self.action_dim)

    def train(self):
        #print "train step",self.time_step
        # Sample a random minibatch of N transitions from replay buffer
        //从经验池里面提取数据用来训练
        minibatch = self.replay_buffer.get_batch(BATCH_SIZE)
        state_batch = np.asarray([data[0] for data in minibatch])
        action_batch = np.asarray([data[1] for data in minibatch])
        reward_batch = np.asarray([data[2] for data in minibatch])
        next_state_batch = np.asarray([data[3] for data in minibatch])
        done_batch = np.asarray([data[4] for data in minibatch])

        # for action_dim = 1
        action_batch = np.resize(action_batch,[BATCH_SIZE,self.action_dim])

        # Calculate y_batch 计算y_batch，实际上就是在运用TD——error公式
        
        next_action_batch = self.actor_network.target_actions(next_state_batch)
        q_value_batch = self.critic_network.target_q(next_state_batch,next_action_batch)
        y_batch = []  
        for i in range(len(minibatch)): 
            if done_batch[i]:
                y_batch.append(reward_batch[i])
            else :
                y_batch.append(reward_batch[i] + GAMMA * q_value_batch[i])
        y_batch = np.resize(y_batch,[BATCH_SIZE,1])
        # Update critic by minimizing the loss L 训练Critic网络
        self.critic_network.train(y_batch,state_batch,action_batch)

        # Update the actor policy using the sampled gradient:
        action_batch_for_gradients = self.actor_network.actions(state_batch)
        q_gradient_batch = self.critic_network.gradients(state_batch,action_batch_for_gradients)
		//训练actor网络
        self.actor_network.train(q_gradient_batch,state_batch)

        # Update the target networks 通过滑动的方式更新
        self.actor_network.update_target()
        self.critic_network.update_target()
//加噪声
    def noise_action(self,state):
        # Select action a_t according to the current policy and exploration noise
        action = self.actor_network.action(state)
        return action+self.exploration_noise.noise()

    def action(self,state):
        action = self.actor_network.action(state)
        return action
//这一步是存储经验，并根据经验值决定是否开始训练
    def perceive(self,state,action,reward,next_state,done):
        # Store transition (s_t,a_t,r_t,s_{t+1}) in replay buffer
        self.replay_buffer.add(state,action,reward,next_state,done)

        # Store transitions to replay start size then start training
        if self.replay_buffer.count() >  REPLAY_START_SIZE:
            self.train()

        #if self.time_step % 10000 == 0:
            #self.actor_network.save_network(self.time_step)
            #self.critic_network.save_network(self.time_step)

        # Re-iniitialize the random process when an episode ends
        //每一回合都要重新更换探索网络的噪声
        if done:
            self.exploration_noise.reset()

//gym_ddpg
import filter_env //这句可以直接删掉
from ddpg import *
import gc
gc.enable()

ENV_NAME = 'InvertedPendulum-v1' //这边可以换一个例如 Pendulum-v0
EPISODES = 100000
TEST = 10

def main():
    #env = filter_env.makeFilteredEnv(gym.make(ENV_NAME)) 
    env=gym.make(ENV_NAME)
    env = env.unwrapped
    env.seed(1)
    agent = DDPG(env)
    #agent = DDPG(env)
    #env.monitor.start('experiments/' + ENV_NAME,force=True)
    //打#的三句话是原来的，我这里改成自己的会更合适。
//回合开始
    for episode in xrange(EPISODES):
        state = env.reset()
        #print "episode:",episode
        # Train
        for step in xrange(env.spec.timestep_limit):
            action = agent.noise_action(state)//加噪声的动作
            next_state,reward,done,_ = env.step(action)//agent与环境互动
            agent.perceive(state,action,reward,next_state,done)//存储经验并训练
            state = next_state//更新状态
            if done:
                break
        # Testing:测试阶段和训练阶段相同
        if episode % 100 == 0 and episode > 100:
			total_reward = 0
			for i in xrange(TEST):
				state = env.reset()
				for j in xrange(env.spec.timestep_limit):
					#env.render()
					action = agent.action(state) # direct action for test
					state,reward,done,_ = env.step(action)
					total_reward += reward
					if done:
						break
			ave_reward = total_reward/TEST
			print 'episode: ',episode,'Evaluation Average Reward:',ave_reward
    env.close()

if __name__ == '__main__':
    main()

总结

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