Create Simulink Environment and Train Agent

This example uses:

This example shows how to convert the PI controller in the watertank Simulink® model to a reinforcement learning deep deterministic policy gradient (DDPG) agent. For an example that trains a DDPG agent in MATLAB®, see Train DDPG Agent to Control Double Integrator System.

Water Tank Model

The original model for this example is the water tank model. The goal is to control the level of the water in the tank. For more information about the water tank model, see watertank Simulink Model (Simulink Control Design).

Modify the original model by making the following changes:

Delete the PID Controller.
Insert the RL Agent block.
Connect the observation vector ${[\begin{matrix} \int e dt & e & h \end{matrix}]}^{T}$ , where $h$ is the height of the tank, $e = r - h$ , and $r$ is the reference height.
Set up the reward $reward = 10 (| e | < 0.1) - 1 (| e | \geq 0.1) - 100 (h \leq 0 | | h \geq 20)$ .
Configure the termination signal such that the simulation stops if $h \leq 0$ or $h \geq 20$ .

The resulting model is rlwatertank.slx. For more information on this model and the changes, see Create Simulink Environments for Reinforcement Learning.

open_system('rlwatertank')

Create Environment Interface

Creating an environment model includes defining the following:

Action and observation signals that the agent uses to interact with the environment. For more information, see rlNumericSpec and rlFiniteSetSpec.
Reward signal that the agent uses to measure its success. For more information, see Define Reward Signals.

Define the observation specification obsInfo and action specification actInfo.

obsInfo = rlNumericSpec([3 1],...
    'LowerLimit',[-inf -inf 0  ]',...
    'UpperLimit',[ inf  inf inf]');
obsInfo.Name = 'observations';
obsInfo.Description = 'integrated error, error, and measured height';
numObservations = obsInfo.Dimension(1);

actInfo = rlNumericSpec([1 1]);
actInfo.Name = 'flow';
numActions = actInfo.Dimension(1);

Build the environment interface object.

env = rlSimulinkEnv('rlwatertank','rlwatertank/RL Agent',...
    obsInfo,actInfo);

Set a custom reset function that randomizes the reference values for the model.

env.ResetFcn = @(in)localResetFcn(in);

Specify the simulation time Tf and the agent sample time Ts in seconds.

Ts = 1.0;
Tf = 200;

Fix the random generator seed for reproducibility.

rng(0)

Create DDPG Agent

Given observations and actions, a DDPG agent approximates the long-term reward using a critic value function representation. To create the critic, first create a deep neural network with two inputs, the observation and action, and one output. For more information on creating a deep neural network value function representation, see Create Policy and Value Function Representations.

statePath = [
    featureInputLayer(numObservations,'Normalization','none','Name','State')
    fullyConnectedLayer(50,'Name','CriticStateFC1')
    reluLayer('Name','CriticRelu1')
    fullyConnectedLayer(25,'Name','CriticStateFC2')];
actionPath = [
    featureInputLayer(numActions,'Normalization','none','Name','Action')
    fullyConnectedLayer(25,'Name','CriticActionFC1')];
commonPath = [
    additionLayer(2,'Name','add')
    reluLayer('Name','CriticCommonRelu')
    fullyConnectedLayer(1,'Name','CriticOutput')];

criticNetwork = layerGraph();
criticNetwork = addLayers(criticNetwork,statePath);
criticNetwork = addLayers(criticNetwork,actionPath);
criticNetwork = addLayers(criticNetwork,commonPath);
criticNetwork = connectLayers(criticNetwork,'CriticStateFC2','add/in1');
criticNetwork = connectLayers(criticNetwork,'CriticActionFC1','add/in2');

View the critic network configuration.

figure
plot(criticNetwork)

Specify options for the critic representation using rlRepresentationOptions.

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

Create the critic representation using the specified deep neural network and options. You must also specify the action and observation specifications for the critic, which you obtain from the environment interface. For more information, see rlQValueRepresentation.

critic = rlQValueRepresentation(criticNetwork,obsInfo,actInfo,'Observation',{'State'},'Action',{'Action'},criticOpts);

Given observations, a DDPG agent decides which action to take using an actor representation. To create the actor, first create a deep neural network with one input, the observation, and one output, the action.

Construct the actor in a similar manner to the critic. For more information, see rlDeterministicActorRepresentation.

actorNetwork = [
    featureInputLayer(numObservations,'Normalization','none','Name','State')
    fullyConnectedLayer(3, 'Name','actorFC')
    tanhLayer('Name','actorTanh')
    fullyConnectedLayer(numActions,'Name','Action')
    ];

actorOptions = rlRepresentationOptions('LearnRate',1e-04,'GradientThreshold',1);

actor = rlDeterministicActorRepresentation(actorNetwork,obsInfo,actInfo,'Observation',{'State'},'Action',{'Action'},actorOptions);

To create the DDPG agent, first specify the DDPG agent options using rlDDPGAgentOptions.

agentOpts = rlDDPGAgentOptions(...
    'SampleTime',Ts,...
    'TargetSmoothFactor',1e-3,...
    'DiscountFactor',1.0, ...
    'MiniBatchSize',64, ...
    'ExperienceBufferLength',1e6); 
agentOpts.NoiseOptions.Variance = 0.3;
agentOpts.NoiseOptions.VarianceDecayRate = 1e-5;

Then, create the DDPG agent using the specified actor representation, critic representation, and agent options. For more information, see rlDDPGAgent.

agent = rlDDPGAgent(actor,critic,agentOpts);

Train Agent

To train the agent, first specify the training options. For this example, use the following options:

Run each training for at most 5000 episodes. Specify that each episode lasts for at most ceil(Tf/Ts) (that is 200) time steps.
Display the training progress in the Episode Manager dialog box (set the Plots option) and disable the command line display (set the Verbose option to false).
Stop training when the agent receives an average cumulative reward greater than 800 over 20 consecutive episodes. At this point, the agent can control the level of water in the tank.

For more information, see rlTrainingOptions.

maxepisodes = 5000;
maxsteps = ceil(Tf/Ts);
trainOpts = rlTrainingOptions(...
    'MaxEpisodes',maxepisodes, ...
    'MaxStepsPerEpisode',maxsteps, ...
    'ScoreAveragingWindowLength',20, ...
    'Verbose',false, ...
    'Plots','training-progress',...
    'StopTrainingCriteria','AverageReward',...
    'StopTrainingValue',800);

Train the agent using the train function. Training is a computationally intensive process that takes several minutes to complete. To save time while running this example, load a pretrained agent by setting doTraining to false. To train the agent yourself, set doTraining to true.

doTraining = false;

if doTraining
    % Train the agent.
    trainingStats = train(agent,env,trainOpts);
else
    % Load the pretrained agent for the example.
    load('WaterTankDDPG.mat','agent')
end

Validate Trained Agent

Validate the learned agent against the model by simulation.

simOpts = rlSimulationOptions('MaxSteps',maxsteps,'StopOnError','on');
experiences = sim(env,agent,simOpts);

Local Function

function in = localResetFcn(in)

% randomize reference signal
blk = sprintf('rlwatertank/Desired \nWater Level');
h = 3*randn + 10;
while h <= 0 || h >= 20
    h = 3*randn + 10;
end
in = setBlockParameter(in,blk,'Value',num2str(h));

% randomize initial height
h = 3*randn + 10;
while h <= 0 || h >= 20
    h = 3*randn + 10;
end
blk = 'rlwatertank/Water-Tank System/H';
in = setBlockParameter(in,blk,'InitialCondition',num2str(h));

end

Documentation

Create Simulink Environment and Train Agent

Water Tank Model

Create Environment Interface

Create DDPG Agent

Train Agent

Validate Trained Agent

Local Function

See Also

Related Topics

Reinforcement Learning Toolbox Documentation

Support