The objective is that the ai from python select the actions in order to meet a reference temperature set in the python code. The actions are up in temperature, down in temperature and the third is do nothing.
- tensorflow or keras
- numpy
- matplotlib
Parameters to on main.py: -im -- (im = implement) - Select implementation to run -sb -- (sb = Start Brain) - Name of brain to start with, from saves/brains') -eb -- (eb = End Brain) - Name of brain to write to after the iterations are done, from saves/brains') -en -- (en = eligibility trace steps n) - How many steps should eligiblity trace steps take (1 is default, is simple one step Q learning)
Forexample, here tensorflow (the currently only ai running) where the end brain that will be saved will be called brainski. Further more is the eligibility trace set to 10
python main.py -im tf -eb brainski -en 10
TCP connect receiver and send port by running Simulink model <TestModel_3a.slx>. (notice first time starting Simulation can result in no connection to TCP/IP server established by python. Simply just run the script again and start the simulation yet again)