no code implementations • 5 Feb 2024 • Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion, Enda Howley, Richard Dazeley, Scott Johnson, Johan Källström, Gabriel Ramos, Roxana Rădulescu, Willem Röpke, Diederik M. Roijers
Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards.
Multi-Objective Reinforcement Learning reinforcement-learning +1
no code implementations • 3 Dec 2023 • Xue Yang, Enda Howley, Micheal Schukat
In this paper, we model thresholding in anomaly detection as a Markov Decision Process and propose an agent-based dynamic thresholding (ADT) framework based on a deep Q-network.
1 code implementation • 9 May 2023 • Willem Röpke, Conor F. Hayes, Patrick Mannion, Enda Howley, Ann Nowé, Diederik M. Roijers
For effective decision support in scenarios with conflicting objectives, sets of potentially optimal solutions can be presented to the decision maker.
no code implementations • 23 Nov 2022 • Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion
Both algorithms outperform the state-of-the-art in multi-objective reinforcement learning for the expected utility of the returns.
Multi-Objective Reinforcement Learning reinforcement-learning +2
no code implementations • 1 Jul 2022 • Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion
In such settings a set of optimal policies must be computed.
no code implementations • 11 Apr 2022 • Mathieu Reymond, Conor F. Hayes, Lander Willem, Roxana Rădulescu, Steven Abrams, Diederik M. Roijers, Enda Howley, Patrick Mannion, Niel Hens, Ann Nowé, Pieter Libin
As decision making in the context of epidemic mitigation is hard, reinforcement learning provides a methodology to automatically learn prevention strategies in combination with complex epidemic models.
no code implementations • 2 Jun 2021 • Conor F. Hayes, Timothy Verstraeten, Diederik M. Roijers, Enda Howley, Patrick Mannion
In this case, to apply multi-objective reinforcement learning, the expected utility of the returns must be optimised.
1 code implementation • 17 Mar 2021 • Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi, Johan 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
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives.
no code implementations • 1 Feb 2021 • Conor F. Hayes, Mathieu Reymond, Diederik M. Roijers, Enda Howley, Patrick Mannion
In many risk-aware and multi-objective reinforcement learning settings, the utility of the user is derived from the single execution of a policy.
no code implementations • 23 Jun 2018 • Seyed Sajad Mousavi, Michael Schukat, Enda Howley
In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing.
no code implementations • 28 Apr 2017 • Seyed Sajad Mousavi, Michael Schukat, Enda Howley
Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces.
no code implementations • 17 Dec 2016 • Sajad Mousavi, Michael Schukat, Enda Howley, Ali Borji, Nasser Mozayani
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e. g., sandwich making and playing the video games).