I would like to ask: the setting of the reward function in the reinforcement learning I would like to ask: when the reward function is composed of different optimization objectives, assuming that the dimensions of these objectives are inconsistent, do you need to dimensionalize these objectives. Many thanks for you! Allow me to introduce an example first. In the paper (doi.org/10.1016/j.trc.2021.103489), the reward in Eq.(17) is compiled like this: r=w1×R1+w2×R2+w3×R3+w4×R4:

where the r1 denotes the driving speed (m/s) while the r3 is the fuel consumption (kw/ton). Thus the two components have different dimensions. How can they be combined directly?

The reward is a unitless quantity you want to maximize. So if $R_v$ is in units of $m/s$, then $w_2$ is in units of $s/m$. (Alternatively, if you prefer, the reward is in units of "utilons" and $w_2$ is in utilon seconds / meter).
