When given several problems to solve in some domain, a standard reinforcement learner learns an optimal policy from scratch for each problem. If the domain has particular characteristics that are goal and problem independent, the learner might be able to take advantage of previously solved problems. Unfortunately, it is generally infeasible to directly apply a learned policy to new problems. This paper presents a method to bias exploration through previous problem solutions, which is shown to speed up learning on new problems. We first allow a Q-learner to learn the optimal policies for several problems. We describe each state in terms of local features, assuming that these state features together with the learned policies can be used to abstract out the domain characteristics from the specific layout of states and rewards in a particular problem. We then use a classifier to learn this abstraction by using training examples extracted from each learned Q-table. The trained classifier maps state features to the potentially goal-independent successful actions in the domain. Given a new problem, we include the output of the classifier as an exploration bias to improve the rate of convergence of the reinforcement learner. We have validated our approach empirically. In this paper, we report results within the complex domain Sokoban which we introduce.

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