基于预测观测插值的多智能体强化学习集中训练混合执行

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Pedro P. Santos , Diogo S. Carvalho , Miguel Vasco , Alberto Sardinha , Pedro A. Santos , Ana Paiva , Francisco S. Melo
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引用次数: 0

摘要

我们研究了多智能体强化学习(MARL)中的混合执行,这是一种智能体旨在利用智能体之间的信息共享来完成在执行时具有任意通信级别的合作任务的范式。在混合执行下,通信级别可以从代理之间不允许通信的设置(完全分散)到具有完全通信的设置(完全集中),但代理事先不知道它们在执行时将遇到哪个通信级别。我们贡献了MARO,一种利用自回归预测模型的方法,以集中的方式训练,来估计缺失代理在执行时的观察值。我们在标准情景和先前基准的扩展中评估MARO,以强调MARL中部分可观测性的影响。实验结果表明,我们的方法始终优于相关基线,允许代理在成功利用共享信息的同时进行错误通信。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Centralized training with hybrid execution in multi-agent reinforcement learning via predictive observation imputation
We study hybrid execution in multi-agent reinforcement learning (MARL), a paradigm where agents aim to complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents. Under hybrid execution, the communication level can range from a setting in which no communication is allowed between agents (fully decentralized), to a setting featuring full communication (fully centralized), but the agents do not know beforehand which communication level they will encounter at execution time. We contribute MARO, an approach that makes use of an auto-regressive predictive model, trained in a centralized manner, to estimate missing agents' observations at execution time. We evaluate MARO on standard scenarios and extensions of previous benchmarks tailored to emphasize the impact of partial observability in MARL. Experimental results show that our method consistently outperforms relevant baselines, allowing agents to act with faulty communication while successfully exploiting shared information.
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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