Learning Heuristic Policies – A Reinforcement Learning Problem

Abstract

How learning heuristic policies may be formulated as a reinforcement learning problem is discussed. Reinforcement learning algorithms are commonly centred around estimating value functions. Here a value function represents the average performance of the learned heuristic algorithm over a problem domain. Heuristics correspond to actions and states to solution instances. The problem of bin packing is used to illustrate the key concepts. Experimental studies show that the reinforcement learning approach is compatible with the current techniques used for learning heuristics. The framework opens up further possibilities for learning heuristics by exploring the numerous techniques available in the reinforcement learning literature.