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2024 , Vol. 41 >Issue 1: 117 - 126

DOI: https://doi.org/10.7523/j.ucas.2022.028

电子信息与计算机科学

面向Ad-Hoc协作的局部观测重建方法

  • 陈皓 ,
  • 杨立昆 ,
  • 尹奇跃 ,
  • 黄凯奇
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  • 1. 中国科学院自动化研究所智能系统与工程研究中心, 北京 100190;
    2. 中国科学院大学人工智能学院, 北京 100049;
    3. 中国科学院脑科学与智能技术卓越创新中心, 上海 200031

收稿日期: 2022-03-02

  修回日期: 2022-04-01

  网络出版日期: 2022-04-01

基金资助

国家自然科学基金(61876181)，北京市科技创新计划(Z19110000119043)，青年创新促进会、中国科学院和中国科学院项目(QYZDB-SSWJSC006)资助

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Local observation reconstruction for Ad-Hoc cooperation

  • CHEN Hao ,
  • YANG Likun ,
  • YIN Qiyue ,
  • HUANG Kaiqi
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  • 1. CRISE, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
    2. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China;
    3 CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China

Received date: 2022-03-02

  Revised date: 2022-04-01

  Online published: 2022-04-01

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摘要

在多智能体强化学习的研究中，如何进行Ad-Hoc协作，也就是说如何适应种类和数量变化的队友，是一个关键问题。现有方法或者有很强的先验知识假设，或者使用硬编码的规则进行合作，缺乏通用性，无法泛化到更一般的Ad-Hoc协作场景。为解决该问题，提出一种面向Ad-Hoc协作的局部观测重建算法，利用注意力机制和采样网络对局部观测进行重建，使得算法认识到并充分利用不同局面中的高维状态表征，实现了在Ad-Hoc协作场景下的零样本泛化。在星际争霸微操环境和Ad-Hoc协作场景上与代表性算法的性能进行对比与分析，验证了算法的有效性。

关键词： 多智能体; 深度强化学习; 信用分配; Ad-Hoc协作

本文引用格式

陈皓 , 杨立昆 , 尹奇跃 , 黄凯奇 . 面向Ad-Hoc协作的局部观测重建方法[J]. 中国科学院大学学报, 2024 , 41(1) : 117 -126 . DOI: 10.7523/j.ucas.2022.028

Abstract

In recent years, multi-agent reinforcement learning has received a lot of attention from researchers. In the study of multi-agent reinforcement learning, the question of how to perform ad-hoc cooperation, i.e., how to adapt to a changing variety and number of teammates, is a key problem. Existing methods either have strong prior knowledge assumptions or use hard-coded protocols for cooperation, which lack generality and can not be generalized to more general ad-hoc cooperation scenarios. To address this problem, this paper proposes a local observation reconstruction algorithm for ad-hoc cooperation, which uses attention mechanisms and sampling networks to reconstruct local observations, enabling the algorithm to recognize and make full use of high-dimensional state representations in different situations and achieve zero-shot generalization in ad-hoc cooperation scenarios. In this paper, the performance of the algorithm is compared and analyzed with representative algorithms on the StarCraft micromanagement environment and ad-hoc cooperation scenarios to verify the effectiveness of the algorithm.

Key words： multi-agent; deep reinforcement learning; credit assignment; Ad-Hoc cooperation

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