JRM Vol.31 No.4 pp. 520-525
doi: 10.20965/jrm.2019.p0520


Sharing Experience for Behavior Generation of Real Swarm Robot Systems Using Deep Reinforcement Learning

Toshiyuki Yasuda* and Kazuhiro Ohkura**

*University of Toyama
3190 Gofuku, Toyama 930-8555, Japan

**Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan

March 22, 2019
June 21, 2019
August 20, 2019
swarm robotics, reinforcement learning, deep Q network, experience sharing, real robot

Swarm robotic systems (SRSs) are a type of multi-robot system in which robots operate without any form of centralized control. The typical design methodology for SRSs comprises a behavior-based approach, where the desired collective behavior is obtained manually by designing the behavior of individual robots in advance. In contrast, in an automatic design approach, a certain general methodology is adopted. This paper presents a deep reinforcement learning approach for collective behavior acquisition of SRSs. The swarm robots are expected to collect information in parallel and share their experience for accelerating their learning. We conducted real swarm robot experiments and evaluated the learning performance of the swarm in a scenario where the robots consecutively traveled between two landmarks.

Behavior learning of a robotic swarm

Behavior learning of a robotic swarm

Cite this article as:
T. Yasuda and K. Ohkura, “Sharing Experience for Behavior Generation of Real Swarm Robot Systems Using Deep Reinforcement Learning,” J. Robot. Mechatron., Vol.31 No.4, pp. 520-525, 2019.
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Last updated on Jul. 12, 2024