An Upper Bound on the Loss from Approximate Optimal-Value Functions
Machine Learning
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Artificial Intelligence
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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This paper investigates the problem of automatically learning how to restructure the reward function of a Markov decision process so as to speed up reinforcement learning. We begin by describing a method that learns a shaped reward function given a set of state and temporal abstractions. Next, we consider decomposition of the per-timestep reward in multieffector problems, in which the overall agent can be decomposed into multiple units that are concurrently carrying out various tasks. We show by example that to find a good reward decomposition, it is often necessary to first shape the rewards appropriately. We then give a function approximation algorithm for solving both problems together. Standard reinforcement learning algorithms can be augmented with our methods, and we show experimentally that in each case, significantly faster learning results.