How to save the best agent during the training of RL?

During the training of a reinforcement learning algorithm, I only want to save the agent that gets the biggest episode reward. what should I do?

[Hitesh]

Hi @sjgege1005,

Could you please let me know what you have tried and where you are facing any issues ?

Answer 1

[Hitesh]

HI @sjgege1005

I am assuming that you have defined necesaary functions "resetEnvironmentAndAgen", "chooseAction" and "stepEnvironment". You need to use MATLAB's "save" function to store the best-performing agent in "bestAgent.mat" whenever a new maximum reward is achieved. Kindly refer to the below code as an example.

function [nextState, reward, isDone] = stepEnvironment(state, action)
    % Replace this with your environment's specific transition logic
    nextState = state + action; 
    reward = -sum(abs(nextState)); 
    isDone = norm(nextState) > 10; 
end

for episode = 1:numEpisodes
    % Reset environment and agent for the new episode
    [state, agent] = resetEnvironmentAndAgent(); % Define this function as needed
    totalReward = 0;
    isDone = false;
    while ~isDone
        % Choose an action based on the current state
        action = chooseAction(agent, state);
        % Take the action in the environment and observe the result
        [nextState, reward, isDone] = stepEnvironment(state, action); 
        % Update total reward
        totalReward = totalReward + reward;
        % Update state
        state = nextState;
    end
    
    % Check if this is the best agent so far
    if totalReward > maxReward
        maxReward = totalReward;
        bestAgent = agent; % Save the best agent
        save('bestAgent.mat', 'bestAgent'); % Save the best agent to a .mat file
    end
end

For more information regarding "Train Reinforcement Learning Agent", kindly refer to the following MATLAB documentation:

https://www.mathworks.com/help/reinforcement-learning/ug/train-reinforcement-learning-agent-in-mdp-environment.html