A local graph clustering algorithm for discovering subgoals in reinforcement learning

Abstract

Reinforcement Learning studies the problem of learning through interaction with the unknown environment. Learning efficiently in large scale problems and complex tasks demands a decomposition of the original complex task to simple and smaller subtasks. In this paper a local graph clustering algorithm is represented for discovering subgoals. The main advantage of the proposed algorithm is that only the local information of the graph is considered to cluster the agent state space. Subgoals discovered by the algorithm are then used to generate skills. Experimental results show that the proposed subgoal discovery algorithm has a dramatic effect on the learning performance. © 2010 Springer-Verlag Berlin Heidelberg.