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.
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References
Bonabeau, E., Dorigo, M., Théraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)
Parrish, J.K., Hamner, W.M., eds.: Animal Groups in Three Dimensions. Cambridge University Press, New York (1997)
Martinoli, A., Mondada, F.: Collective and cooperative group behaviours: Biologically inspired experiments in robotics. In Khatib, O., Salisbury, J.K., eds.: Proceedings of the Fourth International Symposium on Experimental Robotics (1995). Lecture Notes in Control and Information Sciences, Vol. 223. Springer-Verlag, Berlin (1997) 3–10
Ijspeert, A.J., Martinoli, A., Billard, A., Gambardella, L.M.: Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots 11 (2001) 149–171
Lerman, K., Galstyan, A., Martinoli, A., Ijspeert, A.J.: A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life 7 (2001) 375–393
Versino, C., Gambardella, L.M.: Learning real team solutions. In Weiß, G., ed.: Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments. Lecture Notes in Artificial Intelligence, Vol. 1221. Springer-Verlag, Berlin (1997) 40–61
Murciano, A., del R. Millán, J., Zamora, J.: Specialization in multi-agent systems through learning. Biological Cybernetics 76 (1997) 375–382
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Li, L., Martinoli, A., Abu-Mostafa, Y.S. (2002). Emergent Specialization in Swarm Systems. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_43
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DOI: https://doi.org/10.1007/3-540-45675-9_43
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