Selecting the State-Representation in Reinforcement Learning
This addresses a fundamental challenge in reinforcement learning for practitioners dealing with model uncertainty, though it appears incremental as it builds on existing regret minimization frameworks.
The paper tackles the problem of selecting the correct state-representation in reinforcement learning when multiple models are available, achieving a regret of order T^{2/3} relative to the optimal policy.
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.