Improving Multi-Agent Cooperation Using Directed Exploration

机译：通过定向探索改善多主体合作

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In this work, we are addressing the problem of fully cooperative multi-agent system (MASs) with the same common goal for all agents. Coordination question is the main focus in such systems: how to ensure that the agents' own decisions contribute to the group's jointly optimal decisions? To solve this, a new multi-agent reinforcement learning algorithm, named TM LRVS Qlearning, is introduced and tested. The usefulness of this new method is shown using a simulated hunting game.

机译：在这项工作中，我们要解决完全协作的多代理系统（MAS）的问题，所有代理都应具有相同的共同目标。协调问题是此类系统的主要焦点：如何确保代理人的决策有助于团队的共同最优决策？为了解决这个问题，引入并测试了一种新的多主体强化学习算法，名为TM LRVS Qlearning。使用模拟狩猎游戏显示了这种新方法的有用性。

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《International Conference on Computer and Information Sciences》|2020年|1-5|共5页
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Wiem Zemzem; Ines Hosni;
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Reinforcement learning; Multi-agent systems; Games; Mathematical model; Convergence; Testing; Task analysis;

机译：强化学习;多智能体系统;游戏;数学模型;收敛;测试;任务分析;

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4. Formation of cooperation structure by interaction network in directed multi-agent [C] . Kosuke Sekiyama, Yukihisa Okade International joint conference on Autonomous agents and multiagent systems . 2005

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Improving Multi-Agent Cooperation Using Directed Exploration

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