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

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

We study the exploration problem with approximate linear action-value functions in episodic
reinforcement learning under the notion of low inherent Bellman error, a condition normally …

We study the reinforcement learning problem in the setting of finite-horizon1episodic Markov
Decision Processes (MDPs) with S states, A actions, and episode length H. We propose a …

Episodic reinforcement learning and contextual bandits are two widely studied sequential
decision-making problems. Episodic reinforcement learning generalizes contextual bandits …

We present an algorithm based on posterior sampling (aka Thompson sampling) that
achieves near-optimal worst-case regret bounds when the underlying Markov Decision …

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

We consider the task of learning in episodic finite-horizon Markov decision processes with
an unknown transition function, bandit feedback, and adversarial losses. We propose an …

Reinforcement learning encounters many challenges when applied directly in the real world.
Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the …

We consider the problem of preference-based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit …