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 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 reinforcement learning with linear function approximation where the transition
probability and reward functions are linear with respect to a feature mapping $\boldsymbol …

In recent years, deep off-policy actor-critic algorithms have become a dominant approach to
reinforcement learning for continuous control. One of the primary drivers of this improved …

Thompson sampling (TS) is widely used in sequential decision making due to its ease of use
and appealing empirical performance. However, many existing analytical and empirical …

In this work, we study the low-rank MDPs with adversarially changed losses in the full-
information feedback setting. In particular, the unknown transition probability kernel admits a …

In this paper, we prove state-of-the-art Bayesian regret bounds for Thompson Sampling in
reinforcement learning in a multitude of settings. We present a refined analysis of the …

We study reinforcement learning (RL) with linear function approximation, unknown
transition, and adversarial losses in the bandit feedback setting. Specifically, the unknown …

Learning a near optimal policy in a partially observable system remains an elusive
challenge in contemporary reinforcement learning. In this work, we consider episodic …

The first contribution of this paper is the introduction of a new performance measure of a RL
algorithm that is more discriminating than the regret, that we call the regret of exploration that …