We study reinforcement learning (RL) with linear function approximation. For episodic time-
inhomogeneous linear Markov decision processes (linear MDPs) whose transition …

The proximal policy optimization (PPO) algorithm stands as one of the most prosperous
methods in the field of reinforcement learning (RL). Despite its success, the theoretical …

We study time-inhomogeneous episodic reinforcement learning (RL) under general function
approximation and sparse rewards. We design a new algorithm, Variance-weighted …

We investigate the problem of corruption robustness in offline reinforcement learning (RL)
with general function approximation, where an adversary can corrupt each sample in the …

Recently, several studies\citep {zhou2021nearly, zhang2021variance, kim2021improved,
zhou2022computationally} have provided variance-dependent regret bounds for linear …

Aligning large language models (LLM) with human preference plays a key role in building
modern generative models and can be achieved by reinforcement learning from human …

Linear contextual bandits represent a fundamental class of models with numerous real-
world applications, and it is critical to develop algorithms that can effectively manage noise …

We study multi-agent reinforcement learning in the setting of episodic Markov decision
processes, where many agents cooperate via communication through a central server. We …

The exploration-exploitation dilemma has been a central challenge in reinforcement
learning (RL) with complex model classes. In this paper, we propose a new algorithm …

While numerous works have focused on devising efficient algorithms for reinforcement
learning (RL) with uniformly bounded rewards, it remains an open question whether sample …