Model-Based Reinforcement Learning in Factored-State MDPs

We consider the problem of learning in a factored-state Markov decision process that is structured to allow a compact representation. We show that the well-known algorithm, factored Rmax, performs near-optimally on all but a number of timesteps that is polynomial in the size of the compact representation, which is often exponentially smaller than the number of states. This is equivalent to the result obtained by Kearns and Roller for their DBN-E
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algorithm, except that we've conducted the analysis in a more general setting. We also extend the results to a new algorithm, factored IE, that uses the interval estimation approach to exploration and can be expected to outperform factored Rmax on most domains