MATLAB强化学习使用全解析+附代码(以DDPG PPO为例)

iπ弟弟 2024-07-03 08:05:02 阅读 54

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建立动作和观测的数据结构创建环境根据观测、动作、环境step和reset函数创建环境测试环境是否符合要求

网络创建Critic网络设置Critic网络训练参数Actor网络设置Actor网络训练参数

创建智能体设置训练参数开始训练

MATLAB强化学习step函数文件和reset函数文件编写reset函数文件step函数文件注意

MATLAB 强化学习2(以PPO为例)整体代码测试Agent关于PPO的actor

建立动作和观测的数据结构

动作和观测可以分为两种:rlNumericSpecrlFiniteSetSpec

rlNumericSpec:代表连续的动作或观测数据。rlFiniteSetSpec:代表离散的动作或观测数据。

obsInfo = rlNumericSpec([3 1],... % 创建一个3x1的观测矩阵

'LowerLimit',[-inf -inf 0 ]',...

'UpperLimit',[ inf inf inf]');

obsInfo.Name = 'observations';

obsInfo.Description = 'integrated error, error, and measured height';

numObservations = obsInfo.Dimension(1); % 取观测矩阵的维度 这句不要也行

actInfo = rlNumericSpec([1 1]);

actInfo.Name = 'flow';

numActions = actInfo.Dimension(1);% 这句不要也行

HRV_guide实例:

numObs = 8;

ObservationInfo = rlNumericSpec([8 1]);

ObservationInfo.Name = 'States & Delta';

ObservationInfo.Description = 'r, V, lon, lat, gamma, psi, delta_lon, delta_lat';

numAct = 1;

ActionInfo = rlNumericSpec([1 1],...

'LowerLimit',[MIN_action]',...

'UpperLimit',[MAX_action]'); % 连续的输入空间

ActionInfo.Name = 'Guidance Action';

创建环境

根据观测、动作、环境step和reset函数创建环境

env = rlFunctionEnv(obsInfo,actInfo,stepfcn,resetfcn)

stepfcn为自己写的环境的step函数,resetfcn为环境的重置函数。

HRV_guide实例:

env = rlFunctionEnv(ObservationInfo,ActionInfo,'Hyper_Guidance_StepFunction','Hyper_Guidance_ResetFunction');

测试环境是否符合要求

% 测试环境变量

rng(0); % 随机数种子

InitialObs = reset(env)

[NextObs,Reward,IsDone,LoggedSignals] = step(env,simulation_step);

NextObs

Reward

如果正常打印说明至少架构层面没有问题。

网络创建

Critic网络

% 网络创建

hiddenLayerSize = 128;

observationPath = [

imageInputLayer([numObs,1],'Normalization','none','Name','observation')

fullyConnectedLayer(hiddenLayerSize,'Name','fc1')

reluLayer('Name','relu1')

fullyConnectedLayer(hiddenLayerSize,'Name','fc2')

additionLayer(2,'Name','add')

reluLayer('Name','relu2')

fullyConnectedLayer(hiddenLayerSize,'Name','fc3')

reluLayer('Name','relu3')

fullyConnectedLayer(1,'Name','fc4')];

actionPath = [

imageInputLayer([numAct,1],'Normalization','none','Name','action')

fullyConnectedLayer(hiddenLayerSize,'Name','fc5')];

% Create the layer graph.

criticNetwork = layerGraph(observationPath);

criticNetwork = addLayers(criticNetwork,actionPath);

% Connect actionPath to observationPath.

criticNetwork = connectLayers(criticNetwork,'fc5','add/in2');

matlab2020a不支持featureInputLayer,换为imageInputLayeradditionLayer在网络中添加了一个连接点(输入number为2),之后先用layerGraph建图,然后将action的输入网络和critic的基础网络(Q网络需要输入s和a,所以这么设)在连接点连上(criticNetwork = connectLayers(criticNetwork,'fc5','add/in2');)。Critic网络图如下:

!https://s3-us-west-2.amazonaws.com/secure.notion-static.com/b484831b-af84-4354-8736-556b3c90dfe9/rId27.png

设置Critic网络训练参数

criticOptions = rlRepresentationOptions('LearnRate',1e-03,'GradientThreshold',1);

critic = rlQValueRepresentation(criticNetwork,ObservationInfo,ActionInfo,...

'Observation',{ 'observation'},'Action',{ 'action'},criticOptions);

Actor网络

actorNetwork = [

imageInputLayer([numObs,1],'Normalization','none','Name','observation')

fullyConnectedLayer(hiddenLayerSize,'Name','fc1')

reluLayer('Name','relu1')

fullyConnectedLayer(hiddenLayerSize,'Name','fc2')

reluLayer('Name','relu2')

fullyConnectedLayer(hiddenLayerSize,'Name','fc3')

reluLayer('Name','relu3')

fullyConnectedLayer(numAct,'Name','fc4')

tanhLayer('Name','tanh1')];

查看一下Actor a = π(s)网络结构:

plot(layerGraph(actorNetwork))

!https://s3-us-west-2.amazonaws.com/secure.notion-static.com/f7778816-7c98-49de-8094-90ca2721d0a0/rId30.png

设置Actor网络训练参数

actorOptions = rlRepresentationOptions('LearnRate',1e-04,'GradientThreshold',1); % use gpu 'UserDevice','gpu'

actor = rlDeterministicActorRepresentation(actorNetwork,ObservationInfo,ActionInfo,...

'Observation',{ 'observation'},'Action',{ 'tanh1'},actorOptions);

这里 rlDeterministicActorRepresentation 中需要说明观测是网络中的那个节点,动作输出是那个节点。

创建智能体

agentOptions = rlDDPGAgentOptions(...

'SampleTime',simulation_step,...

'TargetSmoothFactor',1e-3,...

'ExperienceBufferLength',1e6 ,...

'DiscountFactor',0.99,...

'MiniBatchSize',256);

agentOptions.NoiseOptions.Variance = 1e-1;

agentOptions.NoiseOptions.VarianceDecayRate = 1e-6;

agent = rlDDPGAgent(actor,critic,agentOptions);

'SampleTime’默认的就是1,具体含义目前不太清楚。

设置训练参数

maxepisodes = 1500;

maxsteps = ceil(Max_simulation_time/simulation_step);

trainingOptions = rlTrainingOptions(...

'MaxEpisodes',maxepisodes,...

'MaxStepsPerEpisode',maxsteps,...

'StopOnError',"on",...

'Verbose',false,...

'Plots',"training-progress",...

'StopTrainingCriteria',"AverageReward",...

'StopTrainingValue',415,...

'ScoreAveragingWindowLength',10,...

'SaveAgentCriteria',"EpisodeReward",...

'SaveAgentValue',415);

这里面有并行多进程的设置,但是并行的toolbox需要另外下载和配置。Verbose设置为true将在命令端打印训练中的reward。

开始训练

trainingStats = train(agent,env,trainingOptions);

MATLAB强化学习step函数文件和reset函数文件编写

reset函数文件

这个函数起始名如下(不需要输入):

function [InitialObservation, LoggedSignal] = Hyper_Guidance_ResetFunction()

LoggedSignal在我看来就是一个记录需要的数据(可能是状态)的句柄,InitialObservation就是初始状态的设置(需要和之前定义的状态维度保持一致。下面给出一个在该函数中返回值的例子:

LoggedSignal.State = [INIT_r/r_scale;INIT_V/V_scale;INIT_theta;INIT_phi;INIT_gamma;INIT_psi;];

InitialObservation = [State_Obs_norm_1;State_Obs_norm_2;State_Obs_norm_3;State_Obs_norm_4;State_Obs_norm_5;State_Obs_norm_6;State_Obs_norm_7;State_Obs_norm_8];

step函数文件

这个函数起始名如下:

function [NextObs,Reward,IsDone,LoggedSignals] = Hyper_Guidance_StepFunction(Action,LoggedSignals)

可以看到step函数传入也有句柄 LoggedSignal,可以理解为通信句柄,在step函数内对它进行更新,所以输出也有 LoggedSignal。给出一个案例:高超声速飞行器在每一次step时通过 LoggedSignal拿到其6个状态变量的值,计算出 NextObs即下一时刻的观测值(注意状态变量时针对飞行器动力学模型而言的,观测值是我们定义在强化学习问题中的,可以理解为一个映射 sobs

Action就是传入的动作(使用它的时候别忘了其维度是之前设置的维度)。

设置好Reward机制和Done条件后这个文件就算写好了。

注意

在创建环境中:rlFunctionEnv(ObservationInfo,ActionInfo,'Hyper_Guidance_StepFunction','Hyper_Guidance_ResetFunction');

传入的函数名称要与我们写的reset和step文件名称相同。

MATLAB 强化学习2(以PPO为例)

matlab PPO既支持连续动作也支持离散动作。

numObs = 3; numAct = 2;

如果是连续动作:

actionInfo = rlNumericSpec([numAct 1],'LowerLimit',-1,'UpperLimit',1); % 连续的动作空间

如果是离散动作:

actionInfo = rlFiniteSetSpec(1:8) % 离散动作空间(8g)

对于观测,这里假设是连续的:

obsInfo = rlNumericSpec([numObs 1],... % 创建一个3x1的观测矩阵

'LowerLimit',[-inf -inf 0 ]',...

'UpperLimit',[ inf inf inf]');

obsInfo.Name = 'observations';

创建环境:

env = rlFunctionEnv(obsInfo,actionInfo,'StepFunction','ResetFunction');

整体代码

numObs = 3; numAct = 2;

obsInfo = rlNumericSpec([numObs 1],... % 创建一个3x1的观测矩阵

'LowerLimit',[-inf -inf 0 ]',...

'UpperLimit',[ inf inf inf]');

obsInfo.Name = 'observations';

% actionInfo = rlNumericSpec([numAct 1],...

% 'LowerLimit',-1,...

% 'UpperLimit',1); % 连续的动作空间

actionInfo = rlFiniteSetSpec([numAct 1]) % 离散动作空间

actionInfo.Name = 'Guidance Action';

env = rlFunctionEnv(obsInfo,actionInfo,'StepFunction','ResetFunction');

rng(0)

criticLayerSizes = [200 100];

actorLayerSizes = [200 100];

criticNetwork = [imageInputLayer([numObs 1 1],'Normalization','none','Name','observation')

fullyConnectedLayer(criticLayerSizes(1),'Name','CriticFC1')

reluLayer('Name','CriticRelu1')

fullyConnectedLayer(criticLayerSizes(2),'Name','CriticFC2')

reluLayer('Name','CriticRelu2')

fullyConnectedLayer(1,'Name','CriticOutput')];

criticOpts = rlRepresentationOptions('LearnRate',1e-3);

critic = rlValueRepresentation(criticNetwork,env.getObservationInfo, ...

'Observation',{ 'observation'},criticOpts);

actorNetwork = [imageInputLayer([numObs 1 1],'Normalization','none','Name','observation')

fullyConnectedLayer(actorLayerSizes(1),'Name','ActorFC1')

reluLayer('Name','ActorRelu1')

fullyConnectedLayer(actorLayerSizes(2),'Name','ActorFC2')

reluLayer('Name','ActorRelu2')

fullyConnectedLayer(numAct,'Name','Action')

tanhLayer('Name','ActorTanh1')

]; % 如为连续动作最后一层fullyConnectedLayer(2*numAct,'Name','Action')

actorOptions = rlRepresentationOptions('LearnRate',1e-3);

actor = rlStochasticActorRepresentation(actorNetwork,env.getObservationInfo,env.getActionInfo,...

'Observation',{ 'observation'}, actorOptions);

opt = rlPPOAgentOptions('ExperienceHorizon',512,...

'ClipFactor',0.2,...

'EntropyLossWeight',0.02,...

'MiniBatchSize',64,...

'NumEpoch',3,...

'AdvantageEstimateMethod','gae',...

'GAEFactor',0.95,...

'DiscountFactor',0.9995);

agent = rlPPOAgent(actor,critic,opt);

trainOpts = rlTrainingOptions(...

'MaxEpisodes',20000,...

'MaxStepsPerEpisode',1200,...

'Verbose',false,...

'Plots','training-progress',...

'StopTrainingCriteria','AverageReward',...

'StopTrainingValue',10000,...

'ScoreAveragingWindowLength',100,...

'SaveAgentCriteria',"EpisodeReward",...

'SaveAgentValue',11000);

trainingStats = train(agent,env,trainOpts);

这里如果动作空间是连续的,那么actor网络的输出维度是numAct即动作维度的两倍(因为对于动作的每一个维度需要生成一个μ和一个σ来产生高斯分布作为策略π)。

step函数和reset函数和ddpg例子中一样,如果是离散的动作,传入的Action为[1, numAct]中某个整数。

测试Agent

只需要load自动保存在savedAgents下的模型即可,名字为saved_agent:

simSteps = 60;

simOptions = rlSimulationOptions('MaxSteps',simSteps);

experience = sim(env,saved_agent,simOptions);

simActionSeries = reshape(experience.Action.GuidanceAction.Data, simSteps+1, 1);

上面这段代码可以获得size为61的Action序列。

关于PPO的actor

ppo的actor在连续动作下输出是num of action的两倍,比如两个连续动作,则输出为4*1,其中第一个维度和第二个维度为动作的均值,第三个和第四个维度是标准差,matlab自带的sim是将这个4维输出建立两个高斯分布再去采样,如果要转c自己写前向传播可以直接取两个均值作为输出即可,这样就成为确定性策略了。



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