most citedDistributed Learning: Sequential Decision Making in Resource-Constrained Environments

4 citations · 8 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ML20214 cited

When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits

Udari Madhushani, Naomi Leonard

In cooperative bandits, a framework that captures essential features of collective sequential decision making, agents can minimize group regret, and thereby improve performance, by…

stat.ML2020

Distributed Bandits: Probabilistic Communication on -regular Graphs

Udari Madhushani, Naomi Ehrich Leonard

We study the decentralized multi-agent multi-armed bandit problem for agents that communicate with probability over a network defined by a -regular graph. Every edge in the grap…

math.OC2020

Heterogeneous Explore-Exploit Strategies on Multi-Star Networks

Udari Madhushani, Naomi Leonard

We investigate the benefits of heterogeneity in multi-agent explore-exploit decision making where the goal of the agents is to maximize cumulative group reward. To do so we study a…

cs.LG20204 cited

Distributed Learning: Sequential Decision Making in Resource-Constrained Environments

Udari Madhushani, Naomi Ehrich Leonard

We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments. For distributed l…

math.OC2020

A Dynamic Observation Strategy for Multi-agent Multi-armed Bandit Problem

Udari Madhushani, Naomi Ehrich Leonard

We define and analyze a multi-agent multi-armed bandit problem in which decision-making agents can observe the choices and rewards of their neighbors under a linear observation cos…