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!
