This page is dedicated to start discussions about the article "Selecting the State-Representation in Reinforcement Learning". Feel free to post any comment, sugggestion, question, correction, extension... I will enjoy discussing this with you.
- Abstract:
" The problem of selecting the right state-representation in a reinforcement learning problem is considered. Several models (functions mapping past observations to a finite set) of the observations are given, and it is known that for at least one of these models the resulting state dynamics are indeed Markovian. Without knowing neither which of the models is the correct one, nor what are the probabilistic characteristics of the resulting MDP, it is required to obtain as much reward as the optimal policy for the correct model (or for the best of the correct models, if there are several). We propose an algorithm that achieves that, with a regret of order T^{2/3} where T is the horizon time."
- Future work:
A natural question is whether the rate T^{2/3} is optimal in this setting. We believe it is not.
