2 citations · 3 across the 10 of their papers we have counts for
11 papers
Predicting Long-Term Self-Rated Health in Small Areas Using Ordinal Regression and Microsimulation
Seán Caulfield Curley, Karl Mason, Patrick Mannion
This paper presents an approach for predicting the self-rated health of individuals in a future population utilising the individuals' socio-economic characteristics. An open-source…
Demonstration-Guided Continual Reinforcement Learning in Dynamic Environments
Xue Yang, Michael Schukat, Junlin Lu +3
Reinforcement learning (RL) excels in various applications but struggles in dynamic environments where the underlying Markov decision process evolves. Continual reinforcement learn…
MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning
Adam Callaghan, Karl Mason, Patrick Mannion
This paper addresses a critical gap in Multi-Objective Multi-Agent Reinforcement Learning (MOMARL) by introducing the first dedicated inner-loop actor-critic framework for continuo…
Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance
Bram Silue, Santiago Amaya-Corredor, Patrick Mannion +2
Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from…
Ireland in 2057: Projections using a Geographically Diverse Dynamic Microsimulation
Seán Caulfield Curley, Karl Mason, Patrick Mannion
This paper presents a dynamic microsimulation model developed for Ireland, designed to simulate key demographic processes and individual life-course transitions from 2022 to 2057.…
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning
Junlin Lu, Patrick Mannion, Karl Mason
Many decision-making problems feature multiple objectives where it is not always possible to know the preferences of a human or agent decision-maker for different objectives. Howev…
Learning in Multi-Objective Public Goods Games with Non-Linear Utilities
Nicole Orzan, Erman Acar, Davide Grossi +2
Addressing the question of how to achieve optimal decision-making under risk and uncertainty is crucial for enhancing the capabilities of artificial agents that collaborate with or…
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
Junlin Lu, Patrick Mannion, Karl Mason
Effective residential appliance scheduling is crucial for sustainable living. While multi-objective reinforcement learning (MORL) has proven effective in balancing user preferences…
Demonstration Guided Multi-Objective Reinforcement Learning
Junlin Lu, Patrick Mannion, Karl Mason
Multi-objective reinforcement learning (MORL) is increasingly relevant due to its resemblance to real-world scenarios requiring trade-offs between multiple objectives. Catering to…
Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
Junlin Lu, Patrick Mannion, Karl Mason
It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems. Demonstration-based preference inference (DemoPI) is a pr…
Go-Explore for Residential Energy Management
Junlin Lu, Patrick Mannion, Karl Mason
Reinforcement learning is commonly applied in residential energy management, particularly for optimizing energy costs. However, RL agents often face challenges when dealing with de…