Abstract
In team Markov games research, it is difficult for an individual agent to calculate the reward of collaborative agents dynamically. We present a coordination tree structure whose nodes are agent subsets or an agent. Two kinds of weights of a tree are defined which describe the cost of an agent collaborating with an agent subset. We can calculate a collaborative agent subset and its minimal cost for collaboration using these coordination trees. Some experiments of a Markov game have been done by using this novel algorithm. The results of the experiments prove that this method outperforms related multi-agent reinforcement-learning methods based on alterable collaborative teams.
This work was supported by the national natural science funds in China with No.61070143 and the science project of Shaanxi with No. 2011K09-28.
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References
Boeling, M.: Multiagent Learning in the Presence of Agents with Limitations. CMU 4, 1–172 (2003)
Parker, L.E.: Distributed algorithms for multi-robot observation of multiple moving targets. Autonomous Robots 12(3), 231–255 (2002)
Pynadath, D.V., Tambe, M.: The communicative multiagent team decision problem: Analyzing teamwork theories and models. Journal of Artificial Intelligence Research 16, 389–423 (2002)
Guestrin, C.: Planning under uncertainty in complex structured environments. PhD thesis, Computer Science Department, Stanford University (August 2003)
Groen, F.C.A., Spaan, M.T.J., Kok, J.R., Pavlin, G.: Real World Multi-agent Systems: Information Sharing, Coordination and Planning. In: ten Cate, B.D., Zeevat, H.W. (eds.) TbiLLC 2005. LNCS (LNAI), vol. 4363, pp. 154–165. Springer, Heidelberg (2007)
Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005)
Tesauro, G.: Extending Q-learning to general adaptive multi-agent systems. In: Advances in Neural Information Processing Systems, vol. 16 (2004)
Guestrin, C., Koller, D., Parr, R.: Multiagent planning with factored MDPs. In: Advances in Neural Information Processing Systems (NIPS) 14. MIT Press (2002a)
Kok, J.R., Vlassis, N.: Collaborative Multiagent Reinforcement Learning by Payoff Propagation. Journal of Machine Learning Research, 1789–1828 (2006)
Christopher Gifford, M., Agah, A.: Sharing in Teams of Heterogeneous,Collaborative Learning Agents. International Journal of Intelligent Systems 24(2), 173–200 (2009)
Zhang, C., Lesser, V.R., Abdallah, S.: Self-organization for coordinating decentralized reinforcement learning. In: Proceedings of AAMAS, pp. 739–746 (2010)
Hoen, P.J.’., de Jong, E.D.: Evolutionary Multi-agent Systems. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 872–881. Springer, Heidelberg (2004)
Kapetanakis, S., Kudenko, D.: Reinforcement learning of coordination in heterogeneous cooperative multi-agent systems. In: Proceedings of the Third Autonomous Agents and Multi-Agent Systems Conference (2004)
Panait, L., Luke, S.: Cooperative Multi-Agent Learning: The State of the Art. Autonomous Agents and Multi-Agent Systems 11(3), 387–434 (2005)
Li, J., Pan, Q., Hong, B.: A new multi-agent reinforcement learning approach. In: 2010 IEEE International Conference on Information and Automation (ICIA), vol. 6, pp. 1667–1671 (2010)
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Min, F., Groen, F.C.A., Hao, L. (2013). Dynamic Partition of Collaborative Multiagent Based on Coordination Trees. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 194. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33932-5_46
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DOI: https://doi.org/10.1007/978-3-642-33932-5_46
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