Autonomous discovery of subgoals using acyclic state trajectories
ICICA'10 Proceedings of the First international conference on Information computing and applications
Learning form experience: a bayesian network based reinforcement learning approach
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
Abstraction in Model Based Partially Observable Reinforcement Learning Using Extended Sequence Trees
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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This paper proposes a novel approach to discover options in the form of stochastic conditionally terminating sequences; it shows how such sequences can be integrated into the reinforcement learning framework to improve the learning performance. The method utilizes stored histories of possible optimal policies and constructs a specialized tree structure during the learning process. The constructed tree facilitates the process of identifying frequently used action sequences together with states that are visited during the execution of such sequences. The tree is constantly updated and used to implicitly run corresponding options. The effectiveness of the method is demonstrated empirically by conducting extensive experiments on various domains with different properties.