Hi Aakash,
I understand that you have a reinforcement learning model that you want to train and deploy the model on the Arduino board.
You can utilize the "Reinforcement Learning Toolbox" in MATLAB to define an RL agent tailored to your hardware setup within a Simulink model. By simulating this model, you can effectively train the agent. Once trained, the model can be deployed on the Arduino board using the "Simulink Support Package for Arduino Hardware".
I recommend training the reinforcement model in Simulink before deploying it on the board rather than directly training on the hardware as Arduino boards, including the Arduino Due, have limited computational resources compared to a full computer. Running an RL algorithm directly on the board is generally impractical due to these constraints. Moreover, direct hardware interaction during training could lead to wear and tear or potential damage if the agent makes unsafe decisions.
Please refer to the following MathWorks documentation for more information:
- https://www.mathworks.com/help/reinforcement-learning/index.html?s_tid=CRUX_lftnav
- https://www.mathworks.com/help/reinforcement-learning/agents.html?s_tid=CRUX_lftnav
- https://www.mathworks.com/help/reinforcement-learning/ug/train-reinforcement-learning-policy-using-custom-training.html
- https://www.mathworks.com/help/simulink/supportpkg-arduino.html
Hope this helps!
