Offline reinforcement learning, which seeks to utilize offline/historical data to optimize
sequential decision-making strategies, has gained surging prominence in recent studies …

Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-
collected dataset without further interactions with the environment. While various algorithms …

We study a Markov matching market involving a planner and a set of strategic agents on the
two sides of the market. At each step, the agents are presented with a dynamical context …

Recent studies in reinforcement learning (RL) have made significant progress by leveraging
function approximation to alleviate the sample complexity hurdle for better performance …

We study offline reinforcement learning (RL) in partially observable Markov decision
processes. In particular, we aim to learn an optimal policy from a dataset collected by a …

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 …

In offline reinforcement learning (RL), the absence of active exploration calls for attention on
the model robustness to tackle the sim-to-real gap, where the discrepancy between the …

As representation learning becomes a powerful technique to reduce sample complexity in
reinforcement learning (RL) in practice, theoretical understanding of its advantage is still …

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 …

Distributionally robust offline reinforcement learning (RL), which seeks robust policy training
against environment perturbation by modeling dynamics uncertainty, calls for function …