How can I code the custom actor loss function in DDPG or TD3? (Reinforcement Learning Toolbox)

I am using RL toolbox to design a controller. The actor loss function of DDPG or TD3 is shown as

I want to modify the loss function with L2 regularization as

I tried to use the setLoss function, but found that it will give an error

I tried to use IsLossSet=1

agent.Actor.IsLossSet=1
agent.Actor.Loss=@rl.loss.???

But don't know how to code this function and I didn't find an example of these problem.

How can I code the custom actor loss function in DDPG or TD3?

Answer 1

[Aditya]

Hi Qingyuan

I understand that you want help in designing controller using RL toolbox.

To code a custom actor loss function in MATLAB for the DDPG (Deep Deterministic Policy Gradient) or TD3 (Twin Delayed Deep Deterministic Policy Gradient) algorithms, you'll need to define the loss function that suits your specific requirements. Here's a step-by-step guide on how to implement a custom actor loss function:

1. Define the custom actor loss function: Create a MATLAB function that calculates the loss for the actor network. The loss function typically takes the actor network's output, the action taken, and any other necessary inputs. The loss function should return a scalar value representing the loss.

2. Create the actor network: Define and create the actor network using the appropriate deep learning framework in MATLAB, such as the Neural Network Toolbox or Deep Learning Toolbox. Make sure to include the necessary layers and configurations for your actor network architecture.

3. Define the optimization algorithm: Specify the optimization algorithm to be used for training the actor network. Common choices include stochastic gradient descent (SGD), Adam, or RMSprop. You can use the `configureTrainingOptions` function to set up the training options, including the optimization algorithm and learning rate.

4. Set the custom loss function: Configure the training options to use the custom actor loss function. Use the `configureCustomActorLoss` function to specify the custom loss function you defined in step 1.

5. Train the actor network: Train the actor network using the custom loss function and the training options you configured. Use your training data and the appropriate training algorithm (e.g., DDPG or TD3) to update the actor network's weights.

Here's a code snippet that demonstrates the general structure of implementing a custom actor loss function in MATLAB for DDPG or TD3:

% Step 1: Define the custom actor loss function 

function loss = customActorLoss(actorOutput, action, otherInputs) 

    % Calculate the loss based on the actor output, action, and other inputs 

    % Return the loss as a scalar value 

    loss = ... % Define your custom loss calculation here 

end 

 

% Step 2: Create the actor network 

actorNetwork = ... % Define and create your actor network using the appropriate deep learning framework 

 

% Step 3: Define the optimization algorithm 

learningRate = 0.001;  % Set the learning rate for the optimization algorithm 

optimizer = adamOptimizationAlgorithm(learningRate); 

 

% Step 4: Set the custom loss function 

trainingOptions = configureTrainingOptions(...);  % Configure the training options 

trainingOptions = configureCustomActorLoss(trainingOptions, @customActorLoss); 

 

% Step 5: Train the actor network 

% Use your training data and the appropriate training algorithm (e.g., DDPG or TD3) 

trainedActor = trainActor(actorNetwork, trainingData, trainingOptions); 

Here is the documentation you may refer to for further details.

Design, train, and simulate reinforcement learning agents - MATLAB (mathworks.com)

Hope this helps.