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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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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