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...
team games/fully cooperative games/multi-agent MDP (MMDP): 全是同构(homogeneous)agent,agent之间互相对称可以交换,并共享一个奖励函数 team-average reward games/networked multi-agent MDP (M-MDP): agent可以拥有不同的奖励函数,但拥有相同的目标,即最大化所有智能体的奖励的平均 stochastic potential games(...
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...
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...
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...
Markov decision process (MDP) A Markov decision process is formalized by the tuple \(\left( {\mathscr {X}}, {\mathscr {U}}, {\mathscr {P}}, R, \gamma \right)\) where \({\mathscr {X}}\) and \({\mathscr {U}}\) are the state and action space, respectively, \({\mathscr ...
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...
We illustrate the performance of our coordinated MDP approach against a Monte-Carlo based planning algorithm intended for large-scale applications, as well as other approaches suitable for allocating medical resources. The evaluations show that the global utility value across all consumer agents is ...
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; 2. 公平目标:Fair RL 引入了一个公平函数 F,以确保各代理之间的奖励公平,而不是最大化单个智能体的奖励总和; 3. α-公平性: 该方法侧重于 α 公平性,它包含各种公平性概念: ...
例如,马尔可夫决策过程(MDP)是单代理强化学习中常用的模型,而在多代理场景中,可能会使用如马尔可夫游戏或其他扩展模型来捕捉多个代理之间的交互和依赖性。这些模型提供了一个基础,使得研究者可以研究和开发适应多代理场景的算法。 边缘计算: 边缘计算是一种计算范式,其核心思想是将数据处理任务从数据中心移动到数据源...