Paper:

# Exploitation-Oriented Learning PS-r^{#}

## Kazuteru Miyazaki^{*} and Shigenobu Kobayashi^{**}

^{*}Department of Assessment and Research for degree Awarding, National Institution for Academic Degrees and University Evaluation, 1-29-1 Gakuennishimachi, Kodaira, Tokyo 187-8587, Japan

^{**}Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa 226-8502, Japan

^{*}, partially observed Markov decision process, Exploitation-oriented Learning XoL

Exploitation-oriented learning (XoL) is a novel approach to goal-directed learning from interaction. Reinforcement learning is much more focused on learning and ensures optimality in Markov decision process (MDP) environments, XoL involves learning a rational policy that obtains rewards continuously and very quickly. PS-r^{*}, a form of XoL, involves learning a useful rational policy not inferior to the random walk in the partially observed Markov decision process (POMDP) where reward types number one. PS-r^{*}, however, requires O(MN^{2}) memory where N is the number of sensory input types and M is an action. We propose PS-r^{#} for learning a useful rational policy in the POMDP using O(MN) memory. PS-r^{#} effectiveness is confirmed in numerical examples.

^{#},”

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.13, No.6, pp. 624-630, 2009.

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