## LESSWRONGLW

This idea was inspired by a discussion with Discord user @jbeshir

Model dynamically inconsistent agents (in particular humans) as having a different reward function at every state of the environment MDP (i.e. at every state we have a reward function that assigns values both to this state and to all other states: we have a reward matrix ). This should be regarded as a game where a different player controls the action at every state. We can now look for value learning protocols that converge to Nash* (or other kind of) equilibrium in this game.

The simpl