how to simulate custom reinforcement learning agent?

I have define custom environment(include step.m and reste.m ), and defined ddpg agent for trainning. After finished learning, i have get the trained agent, how can i export the action sequence from the result agent? in the step.m file, i have define render_plot function to simulate current state. Can i get the action from trained agent and import it to step.m to simulate?

[Ben]

Well, in reinforcement learning toolbox pre-defined agent, we can use sim(env, agent) to get the simulation from the trained agent. But how to deal with the custom environment with self defined step.m and reset.m ?

Answer 1

[Ayush Aniket]

You can extract the action sequence from your trained DDPG agent and use it in your custom environment (step.m) for simulation. Refer the steps below:

1. Once your agent is trained, you can use the getAction function to retrieve actions for given states. You can read more about the function here: https://www.mathworks.com/help/reinforcement-learning/ref/rl.policy.rlmaxqpolicy.getaction.html

% Load trained agent
load('trainedAgent.mat','agent'); % Ensure you have saved the trained agent

% Define initial state
state = reset(env); % Reset environment to get initial state

% Initialize action sequence storage
actionSequence = [];

% Simulate agent actions
for t = 1:numSteps
    action = getAction(agent, state); % Get action from trained agent
    actionSequence = [actionSequence; action]; % Store action
    state = step(env, action); % Apply action to environment
end

2. Now that you have the action sequence, you can pass it to your render_plot function inside step.m:

for t = 1:length(actionSequence)
    render_plot(state, actionSequence(t, :)); % Visualize state-action pair
    state = step(env, actionSequence(t, :)); % Apply action
end