Patrick Mannion

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

Curriculum Learning for Tightly Coupled Multiagent Systems

Opponent Modelling for Reinforcement Learning in Multi-Objective Normal Form Games

Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

Know Your Enemy: Identifying and Adapting to Adversarial Attacks in Deep Reinforcement Learning

Tunable Behaviours in Sequential Social Dilemmas using Multi-Objective Reinforcement Learning

Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning

Parallel Reinforcement Learning for Traffic Signal Control

Fitness Critics for Multiagent Learning: Extended Abstract

Know Your Enemy: Identifying Adversarial Behaviours in Deep Reinforcement Learning Agents (Student Abstract)

Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance

Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from expert...

Predicting Long-Term Self-Rated Health in Small Areas Using Ordinal Regression and Microsimulation

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 microsimulation...

Ireland in 2057: Projections using a Geographically Diverse Dynamic Microsimulation

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. The model...

Demonstration-Guided Continual Reinforcement Learning in Dynamic Environments

Reinforcement learning (RL) excels in various applications but struggles in dynamic environments where the underlying Markov decision process evolves. Continual reinforcement learning (CRL)...

Divide and Conquer: Provably Unveiling the Pareto Front with Multi-Objective Reinforcement Learning

An important challenge in multi-objective reinforcement learning is obtaining a Pareto front of policies to attain optimal performance under different preferences. We introduce Iterated Pareto...

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

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...

Learning in Multi-Objective Public Goods Games with Non-Linear Utilities

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 support humans....