例如,通过在预先收集的离线数据上纯粹地监督训练自回归模型,Decision Transformer [5]避免了通过动态规划计算累计奖励的需求,而是在期望回报、过去状态和动作的条件下生成未来动作。尽管这些方法取得了显着的成功,但它们都没有被设计用于模拟多智能体系统的最困难的(也是MARL所特有的)方面——智能体之间的相互作用。事实...
and then propose the novel architecture of multi-agent decision transformer(MADT)for effective offline learning.MADT leverages the transformer鈥瞫 modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks.A significant benefit of MADT is that it learns...
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...
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,... ...
MAS中的每个智能体可以通过马尔可夫决策过程(Markov Decision Process, MDP)来建模。MDP由以下四元组 :状态空间 :动作空间 :状态转移概率矩阵 :奖励函数 智能体的目标是通过选择最优策略 ,最大化累积奖励: 其中, 是折扣因子, 是在时间步 4.3 LLM与MAS的结合 ...
nations.Thislackofdynamicclinicaldecision-makingabilityisahugeobstaclethatprevents LLMsfromdiagnosinglikedoctors.ThedetailsofFigure5canbefoundinAppendixC. 6FurtherAnalysis 6.1CollaborationMechanism InTable3,wealsoevaluateseveralmodelswithdifferentsettingsofthecooperationmechanism. ...
Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer 2279 18:00 AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners 2167 27:00 Optimal Transport for Offline lmitation Learning 4534 39:00 A Deep Reinforcement Learning Approach for Designing and Optimizing Photonic Chips...
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 ...
An autonomous agent can employ such a model in various ways, with the most significant being its use guiding decision-making processes. related to theory of mind, where agents recursively reason about the states of other agents nested reasoning methods approximated the belief nesting down to a fix...
The environment is usually formulated as a Markov decision-making process with a finite set of states. Formally, the simplest reinforcement learning model consists of a set of environmental states S, a set of actions A, and a set of scalar “gains”. At any time instant t, agent is ...