Shaping multi-agent systems with gradient reinforcement learning
Autonomous Agents and Multi-Agent Systems
Multi-agent systems by incremental gradient reinforcement learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
An investigation into mathematical programming for finite horizon decentralized POMDPs
Journal of Artificial Intelligence Research
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In this paper, we formulate agent's decision process under the framework of Markov decision processes, and in particular, the multi-agent extension to Markov decision process that includes agent communication decisions. We model communication as the way for each agent to obtain local state information in other agents, by paying a certain communication cost. Thus, agents have to decide not only which local action to perform, but also whether it is worthwhile to perform a communication action before deciding the local action. We believe that this would provide a foundation for formal study of coordination activities and may lead to some insights to the design of agent coordination policies, and heuristic approaches in particular. An example problem is studied under this framework and its implications to coordination are discussed.