# Why can’t weight preferences be part of the state in multi objective reinforcement learning?

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A friend and I were looking at the following diagram the other day of a multi-objective Q value network.

*Image Credit: Runzhe Yang*

And my friend, asked: “Why can’t the weight preferences be considered
part of the state?” The network takes both `state`

and `weights`

as
input, so why not just consider `weighs`

as part of the `state`

, which
can be formulated however we want?

I had to go back to the formal definition of a Markov Decision Process.

In an MDP is a 4-tuple *(S, A, P, R)*, where P is the probability of the
next state given a current state and action.

Weight preferences, however, are not part of this equation. Weight preferences typically come from the a user, and in multi-objective reinforcement learning, the problem setting deals with not knowing a user’s weight preferences beforehand.

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