Aligning generative models with human preference via RLHF typically suffers from
overoptimization, where an imperfectly learned reward model can misguide the generative …

This paper investigates model robustness in reinforcement learning (RL) via the framework
of distributionally robust Markov decision processes (RMDPs). Despite recent efforts, the …

Settling the sample complexity of model-based offline reinforcement learning Page 1 The
Annals of Statistics 2024, Vol. 52, No. 1, 233–260 https://doi.org/10.1214/23-AOS2342 © …

We study distributionally robust offline reinforcement learning (RL), which seeks to find an
optimal robust policy purely from an offline dataset that can perform well in perturbed …

This paper concerns the central issues of model robustness and sample efficiency in offline
reinforcement learning (RL), which aims to learn to perform decision making from history …

In this paper, we study offline Reinforcement Learning with Human Feedback (RLHF) where
we aim to learn the human's underlying reward and the MDP's optimal policy from a set of …

In this paper, we consider federated Q-learning, which aims to learn an optimal Q-function
by periodically aggregating local Q-estimates trained on local data alone. Focusing on …

We propose a novel model-based offline Reinforcement Learning (RL) framework, called
Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn …

We investigate the sample efficiency of reinforcement learning in a $\gamma $-discounted
infinite-horizon Markov decision process (MDP) with state space S and action space A …

Reinforcement learning from human feedback (RLHF) has demonstrated great promise in
aligning large language models (LLMs) with human preference. Depending on the …