Conference Paper Profit SharingとRestricted Boltzmann Machineを用いた空間の分節化による学習手法のタクシー配車計画問題へ適用

大久保,将博  ,  市村,匠

Hierarchical Modular Reinforcement Learning (HMRL)[1] consists of 2 layered learning where Profit-Sharing works to plan a target position in the higher layer and Qlearning trains the state-action pair to the target in the lower layer. The method can divide a complex task into subtasks, and it reduces to state dimension and improves learning capability. In order to solve this problem, we propose the learning method based on Restricted Boltzmann Machine (RBM) with subspace divided by Profit Sharing. In this paper, to verify the effectiveness of the proposed method, the assignment problem of taxies was investigated.
開催日:平成28年7月16日 会場:広島大学

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