Reinforcement learning (RL), including multi-armed bandits, is a branch of machine learning
that deals with the problem of sequential decision-making in uncertain and unknown …

This book is written for students and researchers in the field of industrial engineering,
computer science, operations research, management science, electrical engineering, and …

Reinforcement learning is explored as a candidate machine learning technique to enhance
existing analytical solutions for optimal trade execution with elements from the market …

Efficient energy distribution in smart grids is an important problem driven by the need to
manage increasing power consumption across the globe. This problem has been studied in …

We consider the learning dynamics of a single reinforcement learning optimal execution
trading agent when it interacts with an event-driven agent-based financial market model …

The infinite horizon setting is widely adopted for problems of reinforcement learning (RL).
These invariably result in stationary policies that are optimal. In many situations, finite …

This paper studies adaptive bilateral negotiation between software agents in e-commerce
environments. Specifically, we assume that the agents are self-interested, the environment is …

Q-learning is a popular reinforcement learning algorithm. This algorithm has however been
studied and analysed mainly in the infinite horizon setting. There are several important …

We formalize the problem of Structured Prediction as a Reinforcement Learning task. We
first define a Structured Prediction Markov Decision Process (SP-MDP), an instantiation of …

Experience replay is one of the most commonly used approaches to improve the sample
efficiency of reinforcement learning algorithms. In this work, we propose an approach to …