Reinforcement Learning from Human Feedback (RLHF) has been credited as the key
advance that has allowed Large Language Models (LLMs) to effectively follow instructions …

Abstract We study Proportional Response Dynamics (PRD) in linear Fisher markets, where
participants act asynchronously. We model this scenario as a sequential process in which at …

We consider online no-regret learning in unknown games with bandit feedback, where each
player can only observe its reward at each time--determined by all players' current joint …

We study deep reinforcement learning (RL) algorithms with delayed rewards. In many real-
world tasks, instant rewards are often not readily accessible or even defined immediately …

Many practical applications of reinforcement learning require agents to learn from sparse
and delayed rewards. It challenges the ability of agents to attribute their actions to future …

Achieving convergence of multiple learning agents in general $ N $-player games is
imperative for the development of safe and reliable machine learning (ML) algorithms and …

We develop a flexible stochastic approximation framework for analyzing the long-run
behavior of learning in games (both continuous and finite). The proposed analysis template …

In this paper, we provide a general framework for studying multi-agent online learning
problems in the presence of delays and asynchronicities. Specifically, we propose and …

In this paper, we study the problem of learning in quantum games-and other classes of
semidefinite games-with scalar, payoff-based feedback. For concreteness, we focus on the …

Alleviating the delayed feedback problem is of crucial importance for the conversion rate
(CVR) prediction in online advertising. Previous delayed feedback modeling methods using …