151 citations · 215 across the 14 of their papers we have counts for
7 papers · 1 filter
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
Ainur Zhaikhan, Ali H. Sayed
This study proposes the use of a social learning method to estimate a global state within a multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating…
Asynchronous Diffusion Learning with Agent Subsampling and Local Updates
Elsa Rizk, Kun Yuan, Ali H. Sayed
In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each ag…
Diffusion Stochastic Optimization for Min-Max Problems
Haoyuan Cai, Sulaiman A. Alghunaim, Ali H. Sayed
The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need fo…
Multi-Agent Adversarial Training Using Diffusion Learning
Ying Cao, Elsa Rizk, Stefan Vlaski +1
This work focuses on adversarial learning over graphs. We propose a general adversarial training framework for multi-agent systems using diffusion learning. We analyze the converge…
On the Fusion Strategies for Federated Decision Making
Mert Kayaalp, Yunus Inan, Visa Koivunen +2
We consider the problem of information aggregation in federated decision making, where a group of agents collaborate to infer the underlying state of nature without sharing their p…
Policy Evaluation in Decentralized POMDPs with Belief Sharing
Mert Kayaalp, Fatima Ghadieh, Ali H. Sayed
Most works on multi-agent reinforcement learning focus on scenarios where the state of the environment is fully observable. In this work, we consider a cooperative policy evaluatio…