18 citations · 20 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Truly No-Regret Learning in Constrained MDPs
Adrian Müller, Pragnya Alatur, Volkan Cevher +2
Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are bas…
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…
Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes
Adrian Müller, Pragnya Alatur, Giorgia Ramponi +1
Constrained Markov Decision Processes (CMDPs) are one of the common ways to model safe reinforcement learning problems, where constraint functions model the safety objectives. Lagr…
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…