Hi Philipp Hun,
I understand that you are trying to perform custom training for a reinforcement learning (RL) agent within a custom Simulink environment but are facing issues with the "step" function compatibility in Simulink.
Assuming you are using Reinforcement Learning Toolbox and have already created a custom Simulink environment for your RL agent.
To implement custom training of an RL agent in a Simulink environment, you can use the "sim" function to simulate the Simulink model instead of the "step" function. The "sim" function can integrate with the RL agent to provide observations, rewards, and done signals for each simulation step. Here is a simplified example:
% Assuming 'agent' is your RL agent and 'model' is your Simulink model name
for episode = 1:numEpisodes
% Reset the environment to the initial state
simOut = sim(model, 'SimulationMode', 'normal', 'StopTime', '1', ...
'SrcWorkspace', 'current', 'ResetModel', 'on');
% Extract initial observations and other necessary information
% ...
% Run the simulation for each time step until the episode ends
while ~isDone
% Choose an action to take based on the current state
action = chooseAction(agent, observations);
% Apply the action to the environment and simulate one step
simOut = sim(model, 'SimulationMode', 'normal', 'StopTime', '1', ...
'SrcWorkspace', 'current', 'ExternalInput', action, ...
'ResetModel', 'off');
% Extract new observations, reward, and done signal
% ...
% Train the agent using the experience
% ...
end
end
Please note that this is a high-level example and the actual implementation will depend on the specifics of your Simulink model and RL setup.
For more information on simulating Simulink models within MATLAB code, refer to the documentation of the "sim" function.
To understand how to integrate RL agents with Simulink, you can look into the documentation on Reinforcement Learning with Simulink.
Hope this helps!
