Peter Vamplew
Federation University Australia
85 Papers
379 Citations
Peter Vamplew is an academic researcher from Federation University Australia. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 17, co-authored 68 publications. Previous affiliations of Peter Vamplew include University of Tasmania & Hobart Corporation.
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Papers
Survey of intrusion detection systems: techniques, datasets and challenges
TL;DR: A taxonomy of contemporary IDS is presented, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes are presented, and evasion techniques used by attackers to avoid detection are presented.
A survey of multi-objective sequential decision-making
TL;DR: This article surveys algorithms designed for sequential decision-making problems with multiple objectives and proposes a taxonomy that classifies multi-objective methods according to the applicable scenario, the nature of the scalarization function, and the type of policies considered.
A Survey of Multi-Objective Sequential Decision-Making
TL;DR: A survey of multi-objective methods for sequential decision-making problems with multiple objectives can be found in this article, where the authors identify three distinct scenarios in which converting such a problem to a singleobjective one is impossible, infeasible, or undesirable.
Empirical evaluation methods for multiobjective reinforcement learning algorithms
TL;DR: Standard methods for empirical evaluation of multiobjective reinforcement learning algorithms are proposed, and appropriate evaluation metrics and methodologies are proposed for each class.
A practical guide to multi-objective reinforcement learning and planning
Conor Hayes,Roxana Radulescu,Eugenio Bargiacchi,John Källström,Matthew Macfarlane,Mathieu Reymond,Timothy Verstraeten,Luisa M. Zintgraf,Richard Dazeley,Fredrik Heintz,Enda Howley,Athirai A. Irissappane,Patrick Mannion,Ann Nowé,Gabriel Ramos,Marcello Restelli,Peter Vamplew,Diederik M. Roijers +17 more
TL;DR: In this article , a guide to the application of multi-objective decision-making methods to difficult problems is presented, aimed at researchers who are already familiar with singleobjective reinforcement learning and planning methods and who wish to adopt a multiobjective perspective on their research.