We study reinforcement learning (RL) with linear function approximation where the
underlying transition probability kernel of the Markov decision process (MDP) is a linear …

While policy-based reinforcement learning (RL) achieves tremendous successes in practice,
it is significantly less understood in theory, especially compared with value-based RL. In …

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

The success of reinforcement learning in a variety of challenging sequential decision-
making problems has been much discussed, but often ignored in this discussion is the …

We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where
instead of receiving a numeric reward at each step, the RL agent only receives preferences …

We study the safe reinforcement learning problem using the constrained Markov decision
processes in which an agent aims to maximize the expected total reward subject to a safety …

Two central paradigms have emerged in the reinforcement learning (RL) community: online
RL and offline RL. In the online RL setting, the agent has no prior knowledge of the …

Value function approximation has demonstrated phenomenal empirical success in
reinforcement learning (RL). Nevertheless, despite a handful of recent progress on …

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 …

Large language models (LLMs) demonstrate impressive reasoning abilities, but translating
reasoning into actions in the real world remains challenging. In particular, it remains unclear …