Networked reinforcement learning |
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| Authors: | Makito Oku Kazuyuki Aihara |
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| Affiliation: | (1) Department of Mathematical Informatics Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan;(2) Institute of Industrial Science, The University of Tokyo, Tokyo, Japan;(3) Aihara Complexity Modelling Project, ERATO, Japan Science and Technology Agency (JST), Tokyo, Japan |
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| Abstract: | Recently, many models of reinforcement learning with hierarchical or modular structures have been proposed. They decompose a task into simpler subtasks and solve them by using multiple agents. However, these models impose certain restrictions on the topological relations of agents and so on. By relaxing these restrictions, we propose networked reinforcement learning, where each agent in a network acts autonomously by regarding the other agents as a part of its environment. Although convergence to an optimal policy is no longer assured, by means of numerical simulations, we show that our model functions appropriately, at least in certain simple situations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008 |
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| Keywords: | Hierarchical reinforcement learning Modular reinforcement learning POMDP |
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