作为我们发现的自然结果,我们引入了Multi-Agent Transformer (MAT),这是一种编码器-解码器架构,通过序列模型实现通用的多智能体强化学习解决方案。与Decision Transformer [5]不同,MAT是基于试错的在线训练,不需要预先收集演示。重要的是,多智能体优势分解定理的实现确保MAT在训练过程中具有单调性能改进保证。MAT建立了...
基础模型大致setting与Decision Transformer类似 但是优化了DT的输入,DT中是(Rg, s->a),而本文变为了给定(s0, s1, ..., si),要求先去预测Return-to-go的分布,然后从Return-to-go的分布中采样出对应action,同时估计即时reward(作为辅助任务),这样做的优点是通过估计Return-to-go的分布,降低了rtg不确定性对模...
Pre-training modelmulti-agent reinforcement learning(MARL)decision makingtransformeroffline reinforcement learningOffline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment.Such a paradigm is also desirable for multi-age...
Short-range air combat maneuver decision of UAV swarm based on multi-agent Transformer introducing virtual objects Multi-agent transformerVirtual objectReinforcement learningWith the development of Unmanned Aerial Vehicle (UAV) swarm technology, there has been a growing ... F Jiang,M Xu,Y Li,... ...
大模型能否胜任临床诊断任务交互式医学诊断仿真和评测AIHospitalBenchmarkingLargeLanguageModelsinaMulti-agentMedicalInteractionSimulator AIHospital:BenchmarkingLargeLanguageModels inaMulti-agentMedicalInteractionSimulator 112∗33 ZhihaoFan,JialongTang,WeiChen,SiyuanWang,ZhongyuWei, ...
LLM-based Multi-Agent System 2 1.背景介绍 2.核心概念与联系 2.1 语言模型(LLM) 2.2 多智能体系统(MAS) 2.3 LLM与MAS的结合 3.核心算法原理具体操作步骤 3.1 LLM的训练与优化 3.2 MAS的设计与实现 3.3 LLM与MAS的集成 4.数学模型和公式详细讲解举例说明 ...
We discuss multi-task online learning when a decision maker has to deal simultaneously with M tasks. The tasks are related, which is modeled by imposing that the M-tuple of actions taken by the decision maker needs to satisfy certain constraints. We give natural examples of such restrictions ...
As agents produce these actions when they execute in the system, agents are modeled as a function of execution, which yield actions (whose effect is the state transformer function). Thus, a particle agent is defined as:(19) A:RE→AcA:RE→Ac So if an action, say the position update ...
决策型Mask Image Model(Decision-based MIM)是这篇论文中提出的一个核心概念,用于解决神经元分割任务中的一系列挑战。具体来说,决策型MIM有以下几个关键特点: 自动选择遮罩比例和策略:通过使用多智能体强化学习(MARL),该模型能够自动地搜索最适合的图像遮罩比例和遮罩策略,从而消除了手动调整这些参数的需要(第1页和...
Learning in a Multi-agent Environment multiple entities interacting with each other and their shared environment Facilitating social, stable and adaptive behaviors within a multi-agent control learning process is key Benefits of MARL directly address the optimization problem containing multiple decision-makin...