18 citations · 20 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Independent Policy Mirror Descent for Markov Potential Games: Scaling to Large Number of Players
Pragnya Alatur, Anas Barakat, Niao He
Markov Potential Games (MPGs) form an important sub-class of Markov games, which are a common framework to model multi-agent reinforcement learning problems. In particular, MPGs in…
cs.LG2023★ 2 cited
Provably Learning Nash Policies in Constrained Markov Potential Games
Pragnya Alatur, Giorgia Ramponi, Niao He +1
Multi-agent reinforcement learning (MARL) addresses sequential decision-making problems with multiple agents, where each agent optimizes its own objective. In many real-world insta…
cs.LG2019★ 18 cited
Multi-Player Bandits: The Adversarial Case
Pragnya Alatur, Kfir Y. Levy, Andreas Krause
We consider a setting where multiple players sequentially choose among a common set of actions (arms). Motivated by a cognitive radio networks application, we assume that players i…