The rapid changes in the finance industry due to the increasing amount of data have
revolutionized the techniques on data processing and data analysis and brought new …

This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …

Abstract Modern Reinforcement Learning (RL) is commonly applied to practical problems
with an enormous number of states, where\emph {function approximation} must be deployed …

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

Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-
armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson …

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 …

Actor-critic methods are widely used in offline reinforcement learningpractice, but are not so
well-understood theoretically. We propose a newoffline actor-critic algorithm that naturally …

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

This work studies the question of Representation Learning in RL: how can we learn a
compact low-dimensional representation such that on top of the representation we can …

We study model-based offline Reinforcement Learning with general function approximation
without a full coverage assumption on the offline data distribution. We present an algorithm …