Profit SharingとRestricted Boltzmann Machineを用いた空間の分節化による学習手法の提案

Hierarchical Modular Reinforcement Learning (HMRL) consists of 2 layered learning where Profit-Sharing works to plan a target position in the higher layer and Q-learning 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27年7月18日 会場：広島市立大

Reinforcement Learning and Savings Behavior

We show that individual investors over-extrapolate from their personal experience when making savings decisions. Investors who experience particularly rewarding outcomes from 401(k) saving-a high average and/or low variance return-increase their 401(k) savings rate more than investors who have less rewarding experiences. This finding is not driven by aggregate time-series shocks, income effects, rational learning about investing skill, investor fixed effects, or time-varying investor-level heterogeneity that is correlated with portfolio allocations to stock, bond, and cash asset classes. We discuss implications for the equity premium puzzle and interventions aimed at improving household financial outcomes. Copyright (c) 2009 the American Finance Association.

Erkki Koskela* - Rune Stenbacka**

We investigate the interaction between labour and credit market imperfections for equilibrium unemployment in the presence of profit sharing. Our analysis highlights the critical role of the outside option available of employees for the evaluation of the employment implications of intensified credit market competition. In a partial equilibrium with exogenous outside options increased bargaining power of banks has adverse employment effects. In a general equilibrium with endogenous outside options this relationship is frequently reversed; intensified credit market competition increases equilibrium unemployment if the labour market imperfections -- measured by the bargaining power of trade unions - are sufficiently strong

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

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