The manufacturing world is subject to ever-increasing cost optimization pressures.
Maintenance adds to cost and disrupts production; optimized maintenance is therefore of …

In recent years, the research on reinforcement learning (RL) has focused on function
approximation in learning prediction and control of Markov decision processes (MDPs). The …

This book introduces a new approach to the study of systems, living or artificial, that can
learn from experience, and in so doing it indicates a new focus of activity within Artificial …

Policy evaluation is an essential step in most reinforcement learning approaches. It yields a
value function, the quality assessment of states for a given policy, which can be used in a …

A unified view on multi-class support vector machines (SVMs) is presented, covering most
prominent variants including the one-vs-all approach and the algorithms proposed by …

Adaptive filtering algorithms are pervasive throughout signal processing and have had a
material impact on a wide variety of domains including audio processing …

This paper presents an adaptive traffic controller for stochastic road networks with an
integrated model-based and data-driven solution framework. The model-based optimisation …

Learning about many things can provide numerous benefits to a reinforcement learning
system. For example, learning many auxiliary value functions, in addition to optimizing the …

arxiv:2208.11173v3 [cs.AI] 21 Mar 2023 Page 1 1 The Alberta Plan for AI Research Richard S.
Sutton, Michael Bowling, and Patrick M. Pilarski University of Alberta Alberta Machine …

The backtracking line-search is an effective technique to automatically tune the step-size in
smooth optimization. It guarantees similar performance to using the theoretically optimal …