Search Results for author: Enda Howley

Found 12 papers, 2 papers with code

Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning

no code implementations5 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

ADT: Agent-based Dynamic Thresholding for Anomaly Detection

no code implementations3 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.

Anomaly Detection

Distributional Multi-Objective Decision Making

1 code implementation9 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.

Decision Making

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning

no code implementations11 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.

Decision Making Multi-Objective Reinforcement Learning +2

Deep Reinforcement Learning: An Overview

no code implementations23 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.

BIG-bench Machine Learning Deep Learning +5

Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning

no code implementations28 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.

reinforcement-learning Reinforcement Learning +1

Learning to predict where to look in interactive environments using deep recurrent q-learning

no code implementations17 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).

Atari Games Q-Learning +3

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