Modifying Loss Function for Deep Reinforcement Learning Agent

Hi.

Im Looking to use the DQN agent for some initial study of a problem that Im working on. The problem requires that I modify the loss function used in training of the Q-approximation network in DQN. I have the equation that I need to use to modify the loss function, it will be an additional term added to the standard loss function, which is squares sum of target and predicted Q network values. The new term will be like sum of squares of predicted and some expert Q value.

I would like to know if its possible to just modify the loss function, add the new term, and still use the framework presented by Reinforcement Learning Toolbox? Or this is not possible and I would have to atleast write all the code for DQN agents learning part?

Thanks for your time and help!

[Umar]

Hi Syed, In the Reinforcement Learning Toolbox for MATLAB, you can customize the loss function used in training the Q-approximation network of the DQN agent. To incorporate your additional term into the loss function, you can modify the training options and specify your custom loss function. By doing this, you can add the sum of squares of predicted and expert Q values as an extra term in the loss calculation without having to rewrite the entire learning process of the DQN agent. This flexibility allows you to tailor the training process to suit your specific problem requirements while still leveraging the existing framework provided by the Reinforcement Learning Toolbox.

[Syed Adil Ahmed]

Hi, Thanks for the prompt reply.

Im struggling to find where in the training options for RL I can specify my custom loss function?

Is there a specific documentationn page that shows this ? For example in the rlTrainingOptions documentation, there is no place to specify a custom loss function

rlTrainingOptions Documetation - MathWorks

[Umar]

Hi Syed,

In Matlab's Reinforcement Learning Toolbox, specifying a custom loss function within the training options can be a powerful way to tailor the training process to your specific needs. While the official documentation might not explicitly mention how to set a custom loss function in the rlTrainingOptions, you can achieve this by leveraging the flexibility of Matlab's programming environment.

To specify a custom loss function in RL training options, you can follow these steps:

Define Your Custom Loss Function: First, you need to create your custom loss function in Matlab. This function should take the necessary inputs (e.g., predicted values, target values) and compute the loss according to your desired metric. Here is a simple example of a custom loss function in Matlab:

function loss = customLossFunction(predicted, target)

    % Calculate custom loss here

    loss = sum((predicted - target).^2);

end

Incorporate the Custom Loss Function: Once you have defined your custom loss function, you can incorporate it into the RL training process. While the rlTrainingOptions might not have a direct parameter for specifying a custom loss function, you can still use it by integrating your custom loss function into the training loop or algorithm.

Integrate Custom Loss Function in Training Loop: During the training process, you can calculate the loss using your custom function and use it to update the agent's policy. This integration allows you to have full control over how the loss is computed and utilized in the training process.

By following these steps, you can effectively specify and utilize a custom loss function in the RL training options in Matlab, even if it is not explicitly mentioned in the official documentation. Remember that the flexibility of Matlab allows you to extend the functionality of existing tools to meet your specific requirements.

Hope this will help you achieve your goal.

Answer 1

[aditi bagora]

Hi Syed,

I understand that you want to modify the loss function when training your DQN agent.

As suggested by Umar you can define a custom loss function, obtain the loss and supply it to update the agent's policy.

Please refer to the following MathWorks documentation to design and use custom loss functions in general.

• https://www.mathworks.com/help/deeplearning/ug/define-model-gradients-function-for-custom-training-loop.html

Also, you can refer to the following MATLAB Answer that addresses a similar query

• https://www.mathworks.com/matlabcentral/answers/1769475-how-can-i-code-the-custom-actor-loss-function-in-ddpg-or-td3-reinforcement-learning-toolbox

Hope this helps!