This article surveys reinforcement learning from the perspective of optimization and control,
with a focus on continuous control applications. It reviews the general formulation …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

This paper studies model-based reinforcement learning (RL) for regret minimization. We
focus on finite-horizon episodic RL where the transition model $ P $ belongs to a known …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

We consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown
linear system is controlled subject to quadratic costs. Leveraging recent developments in the …

Despite impressive recent advances in reinforcement learning (RL), its deployment in real-
world physical systems is often complicated by unexpected events, limited data, and the …

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 propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based
Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration …

Thompson sampling and other Bayesian sequential decision-making algorithms are among
the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The …

We consider the problem of learning an unknown Markov Decision Process (MDP) that is
weakly communicating in the infinite horizon setting. We propose a Thompson Sampling …