How to use the reinforcement learning toolbox in Matlab to implement delayed reward

I want to implement delayed reward with matlab code. For example, I need to wait until the end of my current episode before giving the reward for each action in this episode. How can I achieve this?

Answer 1

[Shantanu Dixit]

Hi Gongli,

Implementing delayed rewards in MATLAB is an effective way to handle scenarios where the cumulative effect of actions in an episode determines the final reward. This can be achieved using a 'reward buffer' to store rewards during the episode

• Initialize a Reward Buffer: Create an empty buffer at the start of the episode to store rewards.
• Accumulate Rewards: For each step in the episode, calculate the reward based on the current state and action, and store it in the buffer without using it immediately.
• Process Rewards at the End of the Episode: Once the episode ends, calculate the cumulative reward (e.g., sum of rewards in the buffer) and distribute it as a delayed reward.
• Update Policy or Agent: Use the delayed reward to update the policy or agent. This can be handled with a function (here 'applyReward') which integrates the reward signal into the RL algorithm.

Below is a small snippet which shows how this can be implemented logically as part of custom training loop.

rewardBuffer = []; 

for t = 1:episodeLength
    % reward for the current action
    % step function returns reward based on current state and action (user defined)
    [nextObs,reward] = step(state, action); 
    
    % storing the reward in buffer
    rewardBuffer = [rewardBuffer; reward]; 
end

% At the end of the episode
delayedReward = sum(rewardBuffer); 

% Apply the delayed reward as needed 
% (e.g., to update a policy or model, user defined)
applyReward(delayedReward);

This ensures rewards are delayed until the end of the episode and can be appropriately extended to a custom training loop.

Additionally, you can refer to the following MathWorks documentation for more information:

custom class: https://www.mathworks.com/help/reinforcement-learning/ug/create-custom-environment-from-class-template.html

custom training: https://www.mathworks.com/help/releases/R2024a/reinforcement-learning/ug/train-reinforcement-learning-policy-using-custom-training.html

Hope this helps!