利用定向探索改进多智能体合作

2020 2nd International Conference on Computer and Information Sciences (ICCIS) Pub Date : 2020-10-13 DOI:10.1109/ICCIS49240.2020.9257684

Wiem Zemzem, Inès Hosni

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引用次数: 1

摘要

在这项工作中，我们正在解决具有相同共同目标的所有智能体的完全合作多智能体系统(MASs)问题。协调问题是这类系统的主要焦点:如何确保个体自身的决策有助于群体的共同最优决策?为了解决这一问题，本文介绍并测试了一种新的多智能体强化学习算法TM LRVS Qlearning。通过一个模拟狩猎游戏，证明了这种新方法的有效性。

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Improving Multi-Agent Cooperation Using Directed Exploration

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.

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2020 2nd International Conference on Computer and Information Sciences (ICCIS)

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