Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in
an infinite-horizon decision problem, now refers to a general class of reinforcement learning …

Background Decision support systems based on reinforcement learning (RL) have been
implemented to facilitate the delivery of personalized care. This paper aimed to provide a …

A high school student can create deep Q-learning code to control her robot, without any
understanding of the meaning of'deep'or'Q', or why the code sometimes fails. This book is …

This paper analyzes a two-timescale stochastic algorithm framework for bilevel optimization.
Bilevel optimization is a class of problems which exhibits a two-level structure, and its goal is …

Consider a Markov decision process (MDP) that admits a set of state-action features, which
can linearly express the process's probabilistic transition model. We propose a parametric Q …

Robust reinforcement learning (RL) is to find a policy that optimizes the worst-case
performance over an uncertainty set of MDPs. In this paper, we focus on model-free robust …

In this paper we consider deterministic policy gradient algorithms for reinforcement learning
with continuous actions. The deterministic policy gradient has a particularly appealing form …

We present the first deep learning model to successfully learn control policies directly from
high-dimensional sensory input using reinforcement learning. The model is a convolutional …

We study the use of randomized value functions to guide deep exploration in reinforcement
learning. This offers an elegant means for synthesizing statistically and computationally …

When function approximation is used, solving the Bellman optimality equation with stability
guarantees has remained a major open problem in reinforcement learning for decades. The …