Leveraging such loose couplings among agents is key to making coordination in multi-agent systems feasible. In this work, we focus on learning to coordinate. Specifically, we consider the multi-agent multi-armed bandit framework, in which fully cooperative loosely-coupled agents must learn to ...
Paper tables with annotated results for Hierarchical Multi-Agent Multi-Armed Bandit for Resource Allocation in Multi-LEO Satellite Constellation Networks
从问题入手: 1.1 问题描述:Muti-arm Bandits Muti-armed Bandits(多臂老虎机)问题,也叫K-armed Bandit Problem... value) q_estimate是一个1*10的列表,记录agent对每一个老虎机价值的估计值 act()方法是依据算法(我们稍后会探讨这部分内容)选择合适的行动(即选择几号老虎机) step 推荐系统遇上深度学习(十二...
General state/action space. Agent estimates action-values from stream of interaction. How can the agent be confident in its estimates of Q∗ (s, a). Our goal: directed exploration to efficiently estimate Q∗ (s, a). Many model-free methods use uncertainty estimates: (1) Estimate uncerta...
游戏开始后无论选哪个动作做初始动作,对应的奖励都会小于初始估计,那么agent在下一时刻就会选择其他动作。在收敛之前,每个动作都会被选择好几次。 下图展示了使用Q_1(a)=+5的greedy方法在10-armed bandit testbed上的效果。为了方便比较,将Q_1(a)=0的\epsilon-greedy方法作为对照。 可以看到在初始阶段,这种乐观...
In this paper, we introduce a multi-agent multi-armed bandit-based model for ad hoc teamwork with expensive communication. The goal of the team is to maximize the total reward gained from pulling arms of a bandit over a number of epochs. In each epoch, each agent decides whether to pull...
agent multi-armed bandit (MA2B) problem (Liuand Zhao 2010; Anandkumar et al. 2011) is a sequential deci-sion making task consisting of K ∈ N + arms and M ∈ N +agents. In each of the total T ∈ N + decision rounds, eachagent selects one arm to pull and observes its reward ...
Heterogeneous Multi-agent Multi-armed Bandits on Stochastic Block Models no code yet • 11 Feb 2025 Importantly, our regret bounds capture the degree of heterogeneity in the system (an additional layer of complexity), exhibit smaller constants, scale better for large systems, and impose ...
2 Multi-armed Bandit Episodes 2.1 Definitions The multi-armed bandit problem is an interaction between an agent and an environment. A multi-armed bandit episode consists of a sequence of trials. Each episode is associated with a context chosen by the environment from a fixed set Z of po...
Multi-agent reinforcement learning Proximal policy optimization 1. Introduction With the rapid evolution of vehicular communication technologies, humans are being invited into a new era where various driving and entertainment services emerge to improve the experience of drivers and passengers [[1], [2]...