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JACIII Vol.15 No.8 pp. 1109-1115
doi: 10.20965/jaciii.2011.p1109
(2011)

Paper:

Preservation and Application of Acquired Knowledge Using Instance-Based Reinforcement Learning for Multi-Robot Systems

Junki Sakanoue, Toshiyuki Yasuda, and Kazuhiro Ohkura

Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

Received:
March 16, 2011
Accepted:
July 15, 2011
Published:
October 20, 2011
Keywords:
multi-robot systems, reinforcement learning, support vector machine, robustness
Abstract

We have been developing a reinforcement learning technique called BRL as an approach to autonomous specialization, which is a new concept in cooperative multi-robot systems. BRL has a mechanism for autonomously segmenting the continuous state and action space. However, as in other machine learning approaches, overfitting is occasionally observed after successful learning. This paper proposes a technique to sophisticatedly utilize messy knowledge acquired using BRL. The proposed technique is expected to show better robustness against environmental changes. We investigate the proposed technique by conducting computer simulations of a cooperative carrying task.

Cite this article as:
Junki Sakanoue, Toshiyuki Yasuda, and Kazuhiro Ohkura, “Preservation and Application of Acquired Knowledge Using Instance-Based Reinforcement Learning for Multi-Robot Systems,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.8, pp. 1109-1115, 2011.
Data files:
References
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Last updated on Sep. 24, 2021