l-確実探査法:エージェントによる環境同定のための行動選択戦略 : k-確実探査法の不確実性下への拡張

概要

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k-Certainty Exploration Method gives top priority for selection to an action whose number of selection is the fewest. However it doesn't consider any state-transition probability. Therefore, though it guarantees the rationality and the efficiency under deterministic Markov decision processes (MDPs), it doesn't always guarantee the rationality nor the efficiency under stochastic MDPs. In this paper, we propose l-Certainty Exploration Method which is an extension of k-Certainty Exploration Method to stochastic MDPs. We define reachability as a difference between the sampled number of any rule and the sampling number necessary to identify its rule structure by error e with confidence 1. l-Certainty Exploration Method realizes efficient identification of the environment through selecting prior to a rule whose reachability is the lowest. We show the superiority of l-Certainty Exploration Method compared with the other methods through an numerical example.
社団法人人工知能学会の論文
1996-09-01