Recent advances in mean-field games and its multi-population variants enable large-scale heterogeneous multi-agent systems to be modeled through representative agents and their associated mean-field d...
The paper proposes a mean‑field reinforcement learning framework that models rewards and transitions as functions of an unknown low‑dimensional aggregate statistic of a large agent population. By learning this low‑dimensional representation in an offline setting, the authors demonstrate a provable method for obtaining near‑optimal policies. Experiments on a one‑step routing game inspired by supply‑chain problems show that, with a fixed neural‑network size and optimization budget, the learned representation improves reward prediction and the quality of Nash equilibria compared to baselines that ignore population structure.
arXiv:2606. 20062v1 Announce Type: cross Abstract: We introduce optimal coarse correlated equilibria for continuous-time mean field games.
arXiv:2606. 16759v1 Announce Type: new Abstract: We study inverse reinforcement learning for discrete-time, infinite-horizon mean-field games (MFGs) under an average-reward criterion.
arXiv:2605. 03357v2 Announce Type: replace Abstract: Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents.
The paper studies repeated principal-agent games in a multi-armed bandit setting where the principal offers arm-specific incentives, the agent chooses an arm to maximize its own reward plus incentive, and the principal observes only the chosen arm and its own reward. Because the principal cannot see the agent’s reward, traditional estimation fails, so the authors develop an estimator based on the sequence of incentives and chosen arms, analogous to online inverse optimization. Using this estimator, they design a policy that achieves low (square‑root) regret and validate the approach with simulations for personalized incentive design in long‑term statin treatment.
arXiv:2608. 09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond.
arXiv:2609.13469v1 Announce Type: cross Abstract: Understanding how humans respond to incentives, both at the individual and collective levels, is crucial to the design of effective policies. Within...
arXiv:2606. 19883v1 Announce Type: new Abstract: We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model.
arXiv:2608. 13810v1 Announce Type: cross Abstract: We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinatorial data of a game -- its preference graph -- determine the outcomes of no-regret learning dynamics -- such as follow-the-regularized-leader (FTRL).
arXiv:2606. 28824v1 Announce Type: cross Abstract: This paper develops a continuum theory of exit-and-join coalition dynamics in nonatomic cooperative games.
The paper introduces a Lagrangian framework for managing a regenerative commons, framing the problem as a constrained Markov game with a specified depletion budget. It constructs policy sequences from unconstrained solutions, extending time‑average concepts to reset episodes with discounted rewards and terminal costs, and provides theoretical guarantees such as reward‑independent feasibility, cooperative feasibility, and approximate optimality. Experiments on a fishery model using constrained IPPO and MAPPO illustrate how depletion budgets influence stock retention, harvest rewards, and price adaptation.