| Summary: | This paper investigates the problem of automatically learning how torestructure the reward function of a Markov decision process so as tospeed up reinforcement learning. We begin by describing a method thatlearns a shaped reward function given a set of state and temporalabstractions. Next, we consider decomposition of the per-timestepreward in multieffector problems, in which the overall agent can bedecomposed into multiple units that are concurrently carrying outvarious tasks. We show by example that to find a good rewarddecomposition, it is often necessary to first shape the rewardsappropriately. We then give a function approximation algorithm forsolving both problems together. Standard reinforcement learningalgorithms can be augmented with our methods, and we showexperimentally that in each case, significantly faster learningresults.
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