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Selecting the State-Representation in Reinforcement Learning
[article]
2013
arXiv
pre-print
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
arXiv:1302.2552v1
fatcat:fmwjnfv5u5fztjbn27ooia5dm4