Learning Instance-Independent Value Functions to Enhance Local Search

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Robert Moll, Andrew Barto, Theodore Perkins, Richard S. Sutton


Reinforcement learning methods can be used to improve the performance of local search algorithms for combinatorial optimization by learning an evaluation function that predicts the outcome of search. The eval(cid:173) uation function is therefore able to guide search to low-cost solutions better than can the original cost function. We describe a reinforcement learning method for enhancing local search that combines aspects of pre(cid:173) vious work by Zhang and Dietterich (1995) and Boyan and Moore (1997, Boyan 1998). In an off-line learning phase, a value function is learned that is useful for guiding search for multiple problem sizes and instances. We illustrate our technique by developing several such functions for the Dial-A-Ride Problem. Our learning-enhanced local search algorithm ex(cid:173) hibits an improvement of more then 30% over a standard local search algorithm.