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Create Simulink Environment and Train Agent

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 (Reinforcement Learning Toolbox).

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:

  1. Delete the PID Controller.

  2. Insert the RL Agent block.

  3. Connect the observation vector [edteh]T, where h is the height of the tank, e=r-h, and r is the reference height.

  4. Set up the reward reward=10(|e|<0.1)-1(|e|0.1)-100(h0||h20).

  5. Configure the termination signal such that the simulation stops if h0 or h20.

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


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 (Reinforcement Learning Toolbox) and rlFiniteSetSpec (Reinforcement Learning Toolbox).

  • Reward signal that the agent uses to measure its success. For more information, see Define Reward Signals (Reinforcement Learning Toolbox).

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',...

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.


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 Policies and Value Functions (Reinforcement Learning Toolbox).

statePath = [
actionPath = [
commonPath = [

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 contains an axes object. The axes object contains an object of type graphplot.

Specify options for the critic representation using rlRepresentationOptions (Reinforcement Learning Toolbox).

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 (Reinforcement Learning Toolbox).

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 (Reinforcement Learning Toolbox).

actorNetwork = [
    fullyConnectedLayer(3, 'Name','actorFC')

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 (Reinforcement Learning Toolbox).

agentOpts = rlDDPGAgentOptions(...
    'DiscountFactor',1.0, ...
    'MiniBatchSize',64, ...
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 (Reinforcement Learning Toolbox).

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 (Reinforcement Learning Toolbox).

maxepisodes = 5000;
maxsteps = ceil(Tf/Ts);
trainOpts = rlTrainingOptions(...
    'MaxEpisodes',maxepisodes, ...
    'MaxStepsPerEpisode',maxsteps, ...
    'ScoreAveragingWindowLength',20, ...
    'Verbose',false, ...

Train the agent using the train (Reinforcement Learning Toolbox) 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);
    % Load the pretrained agent for the example.

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;
in = setBlockParameter(in,blk,'Value',num2str(h));

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


See Also

(Reinforcement Learning Toolbox)

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