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


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

March 16, 2011
July 15, 2011
October 20, 2011
multi-robot systems, reinforcement learning, support vector machine, robustness
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:
J. Sakanoue, T. Yasuda, and K. 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.
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