I'm new in machine learning topics and I've problem in modeling my environment which has multi parameters with different value ranges and a few actions to perform when value of each parameter is not in normal range.
Each parameter of my environment has to threshold (minTh and maxTh), therefor ranges of its value fit in three region, one is between threshold that is normal region and two is beyond thresholds that are violated regions.
We measure each parameter's value, and when it goes to violated value must take an action to normalize parameter's value. But, the action may effect the others value too (bad or good effect).
My aim is best action selection for each of this situations and make better decision next times a violation occurs with learning effect of actions on parameters.
I think I may choose Markov Decision Process (or its successors) technique to model my environment. My problem is how to model States and Reward function for this environment.
Consider we have two rooms and our parameters is temperature and moisture of these rooms(4 parameters) each have its own thresholds and acceptable values. (for example room One acceptable temperature is between 18 and 30)
I have some actions like turning on or off of air conditioner or heater or etc. in each room, that must perform when value of a parameter is goes to violated region.(example room one temp be 10)
How can I model this environment if I choose Markov Decision Process? or is there any better technique for modeling and solving this problem?