The paper introduces CoDRA, a cost-to-disturbance ratio approach for adversarial reinforcement learning that balances controller performance and disturbance exposure without extra penalty terms. CoDRA uses a self‑normalized actor–critic update, scaling value terms by a stop‑gradient normalization constant derived from the current batch. Experiments on MuJoCo pendulum tasks show that CoDRA achieves the lowest cost across a range of forces and masses, outperforming other methods especially on the more challenging InvertedDoublePendulum environment.
By Taeho Lee, Donghwan Lee
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.
By Panayotis Mertikopoulos
arXiv:2610. 01181v1 Announce Type: new Abstract: We consider stochastic games with independent controlled chains and unknown transition kernels, where players observe only their local states and realized payoffs.
By S. Rasoul Etesami
The paper introduces aspiration-based perturbed learning automata (APLA), a payoff‑based learning scheme that incorporates an aspiration factor to reinforce action selection in distributed multi‑player games. It presents a stochastic stability analysis of APLA in positive‑utility games with noisy observations, establishing that the infinite‑dimensional Markov chain induced by the dynamics can be reduced to a finite‑dimensional one. This work extends previous results beyond potential and coordination games to generic non‑zero‑sum games, with a second part focusing on weakly acyclic games.
By Georgios C. Chasparis
arXiv:2609.06467v1 Announce Type: new
Abstract: In performative reinforcement learning the deployed policy shapes the environment that generates the learner's future data, and the natural solution co...
By Debmalya Mandal
The paper introduces Robust Async-Fed-Q, an epoch‑based federated Q‑learning algorithm that combines variance‑reduced Bellman operator estimation with robust server aggregation. It provides high‑probability finite‑time guarantees showing that honest agents retain sample‑efficiency benefits even when some agents act adversarially, with the adversarial impact diminishing as honest data grows. The authors also present matching lower bounds, extend the framework to single‑trajectory sampling and heterogeneous coverage, and improve communication complexity for asynchronous federated Q‑learning.
By Sreejeet Maity, Aritra Mitra
The paper investigates learning Nash equilibria in partially observable Markov games (POMGs) where agents cannot fully observe the state. By focusing on a subclass with independent state transitions and a Markov potential game structure, the authors propose an independent learning algorithm that allows agents to converge to an approximate Nash equilibrium using only their own observations and actions, without communication. Under a filter stability assumption, finite‑history policies are shown to approximate the POMG sufficiently, enabling a surrogate near‑potential Markov game and yielding quasi‑polynomial sample and computational complexity.
By Philip Jordan, Maryam Kamgarpour
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.
By Gr\'egoire Lambrecht, Mathieu Lauri\`ere
arXiv:2606. 06486v1 Announce Type: new Abstract: In this paper, we study regret minimization in repeated games with \emph{adaptive} opponents who can respond based on histories of play.
By Mingyang Liu, Asuman Ozdaglar, Tiancheng Yu, Kaiqing Zhang
arXiv:2608. 08268v1 Announce Type: cross Abstract: As firms increasingly deploy machine learning for strategic decision-making, understanding algorithmic interactions has become central to operations research and economics.
By Dantong Chu, Xuefeng Gao, Yufei Zhang
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).
By Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos
NashDreamer is a new model-based reinforcement learning framework designed for two-player zero-sum imperfect-information games. It introduces a centralized Multi-Agent Recurrent State-Space Model that separates environment dynamics from player strategy effects, enabling the use of any policy gradient algorithm while preserving convergence guarantees to Nash equilibria. Experiments on four benchmark games show that NashDreamer achieves significantly better sample efficiency than model-free baselines early in training, and the authors analyze its optimization landscape, noting a potential vulnerability to posterior collapse in stochastic settings.
By Tom\'a\v{s} Hole\v{c}ek, Viliam Lis\'y