82 citations · 98 across the 26 of their papers we have counts for
4 papers · 1 filter
Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
Mirco Mutti, Riccardo De Santi, Marcello Restelli +2
Posterior sampling allows exploitation of prior knowledge on the environment's transition dynamics to improve the sample efficiency of reinforcement learning. The prior is typicall…
On Imitation in Mean-field Games
Giorgia Ramponi, Pavel Kolev, Olivier Pietquin +3
We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash eq…
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…