ALA 2010 Co-ChairsProgram Chairs
نویسندگان
چکیده
This paper provides a novel approach to multi-agent coordination in general-sum Markov games. Contrary to what is common in multi-agent learning, our approach does not focus on reaching a particular equilibrium between agent policies. Instead, it learns a basis set of special joint agent policies, over which it can randomize to build different solutions. The main idea is to tackle a Markov game by decomposing it into a set of multi-agent common interest problems, also called Multi-agent Markov Decision Processes (MMDPs). Each MMDP reflects one agent’s preferences in the system. With only a minimum of coordination, simple reinforcement learning agents using Parameterised Learning Automata are able to solve this set of common interest problems in parallel. A third party then selects the MMDP to be played, without a need for the agents to know which problem or reward function they are confronted with. As a result, a team of simple learning agents is able to switch play between desired joint policies rather than mixing individual policies. One application of this principle, which we consider in this paper, is to let simple adaptive agents learn to take turns in generalsum Markov Games in order to satisfy their individual objectives. We experimentally demonstrate this principle in a grid-world setting.
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تاریخ انتشار 2010