Distributional Monte Carlo Tree Search for Risk-Aware and Multi-Objective Reinforcement Learning. - Conor F. Hayes; Mathieu Reymond; Diederik M. Roijers; Enda Howley; Patrick Mannion (AAMAS, 2021)

Risk Aware and Multi-Objective Decision Making with Distributional Monte Carlo Tree Search. - Conor F. Hayes; Mathieu Reymond; Diederik M. Roijers; Enda Howley; Patrick Mannion (CoRR, 2021)

Decision-Theoretic Planning for the Expected Scalarised Returns. - Conor F. Hayes; Diederik M. Roijers; Enda Howley; Patrick Mannion (AAMAS, 2022)

A practical guide to multi-objective reinforcement learning and planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (Auton. Agents Multi Agent Syst., 2022)

Scalar reward is not enough: a response to Silver, Singh, Precup and Sutton (2021). - Peter Vamplew 001; Benjamin J. Smith; Johan Källström; Gabriel de Oliveira Ramos; Roxana Radulescu; Diederik M. Roijers; Conor F. Hayes; Fredrik Heintz; Patrick Mannion; Pieter J. K. Libin; Richard Dazeley; Cameron Foale (Auton. Agents Multi Agent Syst., 2022)

Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models. - Conor F. Hayes; Timothy Verstraeten; Diederik M. Roijers; Enda Howley; Patrick Mannion (CoRR, 2022)

Scalar Reward is Not Enough. - Peter Vamplew 001; Benjamin J. Smith; Johan Källström; Gabriel de Oliveira Ramos; Roxana Radulescu; Diederik M. Roijers; Conor F. Hayes; Friedrik Hentz; Patrick Mannion; Pieter J. K. Libin; Richard Dazeley; Cameron Foale (AAMAS, 2023)

A Brief Guide to Multi-Objective Reinforcement Learning and Planning. - Conor F. Hayes; Roxana Radulescu; Eugenio Bargiacchi; Johan Källström; Matthew Macfarlane; Mathieu Reymond; Timothy Verstraeten; Luisa M. Zintgraf; Richard Dazeley; Fredrik Heintz; Enda Howley; Athirai A. Irissappane; et al. (AAMAS, 2023)

Distributional Multi-Objective Decision Making. - Willem Röpke; Conor F. Hayes; Patrick Mannion; Enda Howley; Ann Nowé; Diederik M. Roijers (IJCAI, 2023)

Monte Carlo tree search algorithms for risk-aware and multi-objective reinforcement learning. - Conor F. Hayes; Mathieu Reymond; Diederik M. Roijers; Enda Howley; Patrick Mannion (Auton. Agents Multi Agent Syst., 2023)

Actor-critic multi-objective reinforcement learning for non-linear utility functions. - Mathieu Reymond; Conor F. Hayes; Denis Steckelmacher; Diederik M. Roijers; Ann Nowé (Auton. Agents Multi Agent Syst., 2023)

Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning. - Peter Vamplew 001; Cameron Foale; Conor F. Hayes; Patrick Mannion; Enda Howley; Richard Dazeley; Scott Johnson; Johan Källström; Gabriel de Oliveira Ramos; Roxana Radulescu; Willem Röpke; Diederik M. Roijers (AAMAS, 2024)

From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models. - Eleni Nisioti; Claire Glanois; Elias Najarro; Andrew Dai 001; Elliot Meyerson; Joachim Winther Pedersen; Laetitia Teodorescu; Conor F. Hayes; Shyam Sudhakaran; Sebastian Risi (CoRR, 2024)

Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment. - Peter Vamplew 001; Conor F. Hayes; Cameron Foale; Richard Dazeley; Hadassah Harland (CoRR, 2024)

Exploring the Pareto front of multi-objective COVID-19 mitigation policies using reinforcement learning. - Mathieu Reymond; Conor F. Hayes; Lander Willem; Roxana Radulescu; Steven Abrams; Diederik M. Roijers; Enda Howley; Patrick Mannion; Niel Hens; Ann Nowé; Pieter Libin (Expert Syst. Appl., 2024)

Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics. - Conor F. Hayes; Felipe Leno da Silva; Jiachen Yang; T. Nathan Mundhenk; Chak Shing Lee; Jacob F. Pettit; Claudio Santiago; Sookyung Kim; Joanne Taery Kim; Ignacio Aravena Solis; Ruben Glatt; Andre R. Goncalves; et al. (CoRR, 2025)

Expected scalarised returns dominance: a new solution concept for multi-objective decision making. - Conor F. Hayes; Timothy Verstraeten; Diederik M. Roijers; Enda Howley; Patrick Mannion (Neural Comput. Appl., 2025)