1 citations · 2 across the 5 of their papers we have counts for
5 papers
Robust Cooperative Multi-Agent Reinforcement Learning:A Mean-Field Type Game Perspective
Muhammad Aneeq uz Zaman, Mathieu Laurière, Alec Koppel +1
In this paper, we study the problem of robust cooperative multi-agent reinforcement learning (RL) where a large number of cooperative agents with distributed information aim to lea…
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective
Muhammad Aneeq uz Zaman, Alec Koppel, Mathieu Laurière +1
We address in this paper Reinforcement Learning (RL) among agents that are grouped into teams such that there is cooperation within each team but general-sum (non-zero sum) competi…
Byzantine-Resilient Decentralized Multi-Armed Bandits
Jingxuan Zhu, Alec Koppel, Alvaro Velasquez +1
In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct stream of rewards, and seeks to exchange information with others to select a sequence of arms…
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search
Wesley A. Suttle, Alec Koppel, Ji Liu
In this work, we propose an information-directed objective for infinite-horizon reinforcement learning (RL), called the occupancy information ratio (OIR), inspired by the informati…
Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits
Jingxuan Zhu, Ethan Mulle, Christopher S. Smith +2
This paper studies a decentralized homogeneous multi-armed bandit problem in a multi-agent network. The problem is simultaneously solved by agents assuming they face a common s…