# How to formulate reward of an rl agent with two objectives

I have started learning reinforcement learning and trying to apply it for my use case. I am developing an rl agent which can maintain temperature at a particular value, and minimize the energy consumption if equipment by taking different actions that are available for it to take.

I am trying to formulate a reward function for it.

energy and temp_act can be measured

energy_coeff = -10
temp_coeff = -10

temp_penalty = np.abs(temp_setpoint - temp_act)

reward = energy_coeff * energy + temp_coeff * temp_penalty

This is the reward function I am using, but intuitively , I feel it should be better. because absolute value of enenrgy and temp_penalty are on different scales. How do i take into count the scaling problem, while structuring a reward.

As your penalties are on different scales and in different units, it is your task as the engineer setting the objective to provide the conversion to a single scale. That is what the coefficients represent - you can even think of them as $$points/Joule$$ for energy and $$points/(\Delta K)$$ for temperature difference.
Sometimes analysis will show you that there is a natural combined scale. For instance, in business settings it may be possible to frame compromises as financial costs, e.g. your coefficients might be $$\text{GBP}/Joule$$ for energy and $$\text{GBP}/(\Delta K)$$ for temperature difference. Then you have one clear objective to minimise cost or maximise profit.