Emergent Specialization in Swarm Systems
Abstract
Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent has only partial information about the global task. In this paper, we investigate the influence of different reinforcement signals (local and global) and team diversity (homogeneous and heterogeneous agents) on the learned solutions. We compare the learned solutions with those obtained by systematic search in a simple case study in which pairs of agents have to collaborate in order to solve the task without any explicit communication. The results show that policies which allow teammates to specialize find an adequate diversity of the team and, in general, achieve similar or better performances than policies which force homogeneity. However, in this specific case study, the achieved team performances appear to be independent of the locality or globality of the reinforcement signal.
Additional Information
© 2002 Springer-Verlag Berlin Heidelberg. First Online: 20 August 2002. We would like to acknowledge Lavanya Reddy and Eric Tuttle for having implemented a first version of the Δ-method we are using in this paper. This work was supported by the Caltech Center for Neuromorphic Systems Engineering under NSF Cooperative Agreement EEC-9402726.Additional details
- Eprint ID
- 96895
- DOI
- 10.1007/3-540-45675-9_43
- Resolver ID
- CaltechAUTHORS:20190702-150156265
- NSF
- EEC-9402726
- Center for Neuromorphic Systems Engineering, Caltech
- Created
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2019-07-08Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Series Name
- Lecture Notes in Computer Science
- Series Volume or Issue Number
- 2412