Multi-Agent MDP Homomorphic Networksopenreview.net/pdf?id=H7HDG--DJF0 原文特色 本文介绍了多智能体 MDP 同态网络,这是一类允许仅使用本地信息进行分布式执行的网络,但能够在协作多智能体系统的联合状态-动作空间中共享全局对称性之间的经验。在协作多智能体系统中,智能体的不同配置及其局部观察之间会出现复杂...
综上,用户对于智能家居的期望可以总体归纳为安全、舒适、易用、节能、健康等几个维度,又可根据不同的场景进行细化,由此得到用户的总期望值Et,或者在特定场景下的期望值En,单智能体强化学习(Single Agent Reinforcement Learning,SARL)中智能体与环境的交互遵循马尔可夫决策...
一个MPD可以被形式化地定义为:(S,A,P,R,\gamma) 当有多个Agent参与时,由于其他的Agents的动作与整体环境状态强烈相关,一个MDP不再适用于 描述环境。于是有了MDP的推广:Markov博弈。也叫随机博弈(Stochastic games)。 一个Markov Game(MGs)形式化地被定义为(N,S,{A^i}{i\in N},P,{R^i}{i\in N},...
We extend this result to the framework of Multi-Agent MDP's, a straightforward extension of single-agent MDP's to distributed cooperative multi-agent decision problems. Furthermore, we combine this result with the application of parametrized learning automata yielding global optimal converg...
The fundamental goal of an MDP is to determine the most effective policy that optimizes the total reward over a series of decision-making steps. This cumulative reward, commonly known as the expected return, is calculated by summing the rewards obtained from each action, with future rewards disc...
The fundamental goal of an MDP is to determine the most effective policy that optimizes the total reward over a series of decision-making steps. This cumulative reward, commonly known as the expected return, is calculated by summing the rewards obtained from each action, with future rewards disc...
On the contrary, one of the fundamental problems in the multi-agent domain is that agents update their policies during the learning process simultaneously, such that the environment appears non-stationary from the perspective of a single agent. Hence, the Markov assumption of an MDP no longer hol...
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 ...
公平强化学习(Fair RL )是一种创新的强化学习算法设计方法,除了传统的奖励最大化之外,还能优化多个智能体或目标之间的公平性。这种方法可满足多代理系统(如资源分配或决策过程)对公平结果的需求。 关键概念 1. 多智能体MDP:问题被表述为多代理马尔可夫决策过程(MDP),其中多个智能体与环境相互作用; ...
对于多目标MDP,可以通过一个双时段过程(a two-timescale process)使用ascalarisation function来转为单目标MDP。 2.3 解马尔可夫过程 解马尔可夫过程,本质上是最大化获取高奖励的概率。这个概率的度量用这么一个折扣累积占位量来表示: 其中双线体1表示指示函数,即在满足条件时为1,否则为0。μ^{\pi}(s,a)的含义...