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:
Delete the PID Controller.
Insert the RL Agent block.
Connect the observation vector , where is the height of the tank, , and is the reference height.
Set up the reward .
Configure the termination signal such that the simulation stops if or .
The resulting model is rlwatertank.slx
. For more information on this model and the changes, see Create Simulink Reinforcement Learning Environments (Reinforcement Learning Toolbox).
open_system('rlwatertank')
Create the 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) andrlFiniteSetSpec
(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"; actInfo = rlNumericSpec([1 1]); actInfo.Name = "flow";
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 the Critic
Given observations and actions, a DDPG agent approximates the long-term reward using a value function approximator as a critic.
Create a deep neural network to approximate the value function within the critic. To create a network with two inputs, the observation and action, and one output, the value, use three different paths, and specify each path as a row vector of layer objects. You can obtain the dimension of the observation and action spaces from the obsInfo
and actInfo
specifications.
statePath = [ featureInputLayer(obsInfo.Dimension(1),Name="netObsIn") fullyConnectedLayer(50) reluLayer fullyConnectedLayer(25,Name="CriticStateFC2")]; actionPath = [ featureInputLayer(actInfo.Dimension(1),Name="netActIn") fullyConnectedLayer(25,Name="CriticActionFC1")]; commonPath = [ additionLayer(2,Name="add") reluLayer 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)
Convert the network to a dlnetwork
object and summarize its properties.
criticNetwork = dlnetwork(criticNetwork); summary(criticNetwork)
Initialized: true Number of learnables: 1.5k Inputs: 1 'netObsIn' 3 features 2 'netActIn' 1 features
Create the critic approximator object using the specified deep neural network, the environment specification objects, and the names if the network inputs to be associated with the observation and action channels.
critic = rlQValueFunction(criticNetwork,obsInfo,actInfo, ... ObservationInputNames="netObsIn", ... ActionInputNames="netActIn");
For more information on Q-value function objects, see rlQValueFunction
(Reinforcement Learning Toolbox).
Check the critic with a random input observation and action.
getValue(critic, ... {rand(obsInfo.Dimension)}, ... {rand(actInfo.Dimension)})
ans = single
-0.1631
For more information on creating critics, see Create Policies and Value Functions (Reinforcement Learning Toolbox).
Create the Actor
Given observations, a DDPG agent decides which action to take using a deterministic policy, which is implemented by an actor.
Create a deep neural network to approximate the policy within the actor. To create a network with one input, the observation, and one output, the action, a row vector of layer objects. You can obtain the dimension of the observation and action spaces from the obsInfo
and actInfo
specifications.
actorNetwork = [ featureInputLayer(obsInfo.Dimension(1)) fullyConnectedLayer(3) tanhLayer fullyConnectedLayer(actInfo.Dimension(1)) ];
Convert the network to a dlnetwork
object and summarize its properties.
actorNetwork = dlnetwork(actorNetwork); summary(actorNetwork)
Initialized: true Number of learnables: 16 Inputs: 1 'input' 3 features
Create the actor approximator object using the specified deep neural network, the environment specification objects, and the name if the network input to be associated with the observation channel.
actor = rlContinuousDeterministicActor(actorNetwork,obsInfo,actInfo);
For more information, see rlContinuousDeterministicActor
(Reinforcement Learning Toolbox).
Check the actor with a random input observation.
getAction(actor,{rand(obsInfo.Dimension)})
ans = 1x1 cell array
{[-0.3408]}
For more information on creating critics, see Create Policies and Value Functions (Reinforcement Learning Toolbox).
Create the DDPG Agent
Create the DDPG agent using the specified actor and critic approximator objects.
agent = rlDDPGAgent(actor,critic);
For more information, see rlDDPGAgent
(Reinforcement Learning Toolbox).
Specify options for the agent, the actor, and the critic using dot notation.
agent.SampleTime = Ts; agent.AgentOptions.TargetSmoothFactor = 1e-3; agent.AgentOptions.DiscountFactor = 1.0; agent.AgentOptions.MiniBatchSize = 64; agent.AgentOptions.ExperienceBufferLength = 1e6; agent.AgentOptions.NoiseOptions.Variance = 0.3; agent.AgentOptions.NoiseOptions.VarianceDecayRate = 1e-5; agent.AgentOptions.CriticOptimizerOptions.LearnRate = 1e-03; agent.AgentOptions.CriticOptimizerOptions.GradientThreshold = 1; agent.AgentOptions.ActorOptimizerOptions.LearnRate = 1e-04; agent.AgentOptions.ActorOptimizerOptions.GradientThreshold = 1;
Alternatively, you can specify the agent options using an rlDDPGAgentOptions
(Reinforcement Learning Toolbox) object.
Check the agent with a random input observation.
getAction(agent,{rand(obsInfo.Dimension)})
ans = 1x1 cell array
{[-0.7926]}
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 mostceil(Tf/Ts)
(that is200
) time steps.Display the training progress in the Episode Manager dialog box (set the
Plots
option) and disable the command line display (set theVerbose
option tofalse
).Stop training when the agent receives an average cumulative reward greater than
800
over20
consecutive episodes. At this point, the agent can control the level of water in the tank.
For more information, see rlTrainingOptions
(Reinforcement Learning Toolbox).
trainOpts = rlTrainingOptions(... MaxEpisodes=5000, ... MaxStepsPerEpisode=ceil(Tf/Ts), ... ScoreAveragingWindowLength=20, ... Verbose=false, ... Plots="training-progress",... StopTrainingCriteria="AverageReward",... StopTrainingValue=800);
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); 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=ceil(Tf/Ts),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
See Also
train
(Reinforcement Learning Toolbox)
Related Topics
- Train Reinforcement Learning Agents (Reinforcement Learning Toolbox)
- Create Simulink Reinforcement Learning Environments (Reinforcement Learning Toolbox)