how to write an RL with delayed reward at the end of episode using class template

Hi, I am having a problem with RL with delayed reward. I am using the Matlab helper (class) for environment. I do not know how to handle reward so that at the end of episode the reward is used for updating the parameters. More specifically, when using class template, I have step, reset,... functions. when the parameters is updated? is it after running step function? I wrote the reward in the step function. but I need to update the parameters only at the end of episode.

Answer 1

[Aravind]

Hi @MOHAMMADREZA,

To handle delayed rewards in a reinforcement learning (RL) setup using a class template in MATLAB, you can structure your code to accumulate rewards throughout the episode and update the parameters only at the episode's end. This method is common for episodic tasks where the reward is determined upon episode completion.

Here is a general approach you can follow:

1. During each step of the episode, accumulate the reward instead of using it immediately to update the agent's parameters.
2. At each step, check if the episode has ended. If it has, update your parameters based on the accumulated reward.
3. Implement a method specifically for updating parameters based on the accumulated reward once the episode ends.

Below is a simplified example to illustrate this:

classdef MyEnvironment < rl.env.MATLABEnvironment
    properties
        % Define properties for state, cumulative reward, etc.
        State
        CumulativeReward
    end
    
    methods
        function this = MyEnvironment()
            % Constructor
            this.State = initialState();
            this.CumulativeReward = 0;
        end
        
        function [nextState, reward, isDone, loggedSignals] = step(this, action)
            % Define the step function
            [nextState, reward, isDone, loggedSignals] = takeAction(this, action);
            this.CumulativeReward = this.CumulativeReward + reward;
            
            if isDone
                % Update parameters at the end of the episode
                updateParameters(this);
            end
        end
        
        function reset(this)
            % Reset the environment for a new episode
            this.State = initialState();
            this.CumulativeReward = 0;
        end
        
        function updateParameters(this)
            % Update the parameters based on the cumulative reward
            % Implement your parameter update logic here
        end
        
        function state = initialState(this)
            % Define the initial state of the environment
            state = ...; % Your initial state logic
                end
            
            function [nextState, reward, isDone, loggedSignals] = takeAction(this, action)
                % Define how the environment responds to an action
                nextState = ...; % Your next state logic
                    reward = ...; % Your reward logic
                    isDone = ...; % Your termination condition
                    loggedSignals = ...; % Any additional signals to log
                    end
            end
        end

Using this structure, you should be able to implement delayed rewards effectively in your RL environment. For more information about creating custom environments from class templates, refer to the following documentation page: https://www.mathworks.com/help/reinforcement-learning/ug/create-custom-environment-from-class-template.html.

I hope this answers your query. If you provide more details about your specific environment and other aspects, I can offer better advice.