In MATLAB/Simulink, when working with reinforcement learning, you can save the training data (such as the agent's experience replay buffer, training statistics, and learned policy) and later reload it to continue training. Here's how you can approach this:
Save Training Data
% Assume 'agent' is your trained reinforcement learning agent
% and 'trainingStats' is the output from the 'train' function.
save('trainedAgent.mat', 'agent', 'trainingStats');
Loading and Continuing Training
% Load the trained agent and training statistics
load('trainedAgent.mat', 'agent', 'trainingStats');
% Continue training the agent
[agent, trainingStats] = train(env, agent, trainingOptions);
Adding a Timer for Reward Function in Simulink:
To add a timer for a reward function in Simulink, you can use Simulink blocks to keep track of time and use this information in your reward calculation. Here's a general approach:
- Add a Clock Block: Use a Clock block to provide the current simulation time.
- Integrate Timer Logic: Depending on your reward function's requirements, you might use additional blocks (like Relational Operator, Math Function, or Logic blocks) to implement logic that determines when to give a reward based on the elapsed time.
- Implement Reward Function: Use a MATLAB Function block or an Interpreted MATLAB Function block to implement the reward function, which takes the timer information as an input and calculates the reward based on your criteria.
Here's an example of what the MATLAB Function block might contain:
function reward = calculateReward(timeElapsed, otherInputs)
% Implement your reward function logic here
% For example, give a reward if timeElapsed is within a certain range
if timeElapsed < someThreshold
reward = someRewardValue;
else
reward = 0;
end
end
