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
1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima 739-8527, Japan
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.
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