The paper studies a repeated principal–agent game where the principal cannot observe the agent’s rewards and both parties learn over time. It introduces a non‑parametric estimator that uses only the history of incentives and choices, and combines it with a data‑driven incentive policy in a multi‑armed bandit setting. The authors prove finite‑sample consistency of the estimator and provide a regret bound for the principal, supported by simulations on green‑energy aggregator contracts.
By Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani
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: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.
By Ananya Kunisetty, Avishek Ghosh
The paper studies the trade‑off between maximizing rewards and accurately estimating current treatment effects in nonstationary multi‑armed bandits. For $K$‑armed bandits with H"older‑smooth mean rewards, the authors derive minimax bounds on expected dynamic regret under a high‑probability simultaneous coverage requirement for all current effects. They propose the TRACE algorithm, which achieves a regret–precision trade‑off that separates reward‑learning regret from the unavoidable cost of refreshing current‑effect estimates, and extend the method with an empirical‑Bernstein e‑process to adapt exploration while preserving worst‑case guarantees.
By Sharan Sahu, Martin T. Wells
We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation. At each round, the learner observes a context, selects a combinatorial action consisting of a subset of basic arms, and receives the reward of each selected arm; the goal is to maximize the cumulative reward over time.
The paper investigates how to infer agents’ underlying incentives in strategic multi‑agent settings, where traditional inverse reinforcement learning (IRL) may fail to produce rewards that guide optimal play. It introduces the concept of strategic identifiability and shows that learning from a fixed pair of policies can be insufficient regardless of sample size. To address this, the authors propose an active IRL algorithm for two‑player repeated Stackelberg games, combining maximum‑likelihood estimation with experimental design to achieve finite‑sample guarantees on sub‑optimality, and validate the approach in an anti‑poaching scenario.
By Antoine Bergerault, Cyrille Kone, Giorgia Ramponi