Portal-based sound propagation for first-person computer games. - Cameron Foale; Peter Vamplew 001 (IE, 2007)

Reinforcement Learning of Pareto-Optimal Multiobjective Policies Using Steering. - Peter Vamplew 001; Rustam Issabekov; Richard Dazeley; Cameron Foale (Australasian Conference on Artificial Intelligence, 2015)

Softmax exploration strategies for multiobjective reinforcement learning. - Peter Vamplew 001; Richard Dazeley; Cameron Foale (Neurocomputing, 2017)

Steering approaches to Pareto-optimal multiobjective reinforcement learning. - Peter Vamplew 001; Rustam Issabekov; Richard Dazeley; Cameron Foale; Adam Berry; Tim Moore; Douglas C. Creighton (Neurocomputing, 2017)

Modeling neurocognitive reaction time with gamma distribution. - Meena Santhanagopalan; Madhu Chetty; Cameron Foale; Sunil Aryal; Britt Klein (ACSW, 2018)

Relevance of Frequency of Heart-Rate Peaks as Indicator of 'Biological' Stress Level. - Meena Santhanagopalan; Madhu Chetty; Cameron Foale; Sunil Aryal; Britt Klein (ICONIP (7), 2018)

SoniFight: Software to Provide Additional Sonification Cues to Video Games for Visually Impaired Players. - Alastair Lansley; Peter Vamplew 001; Cameron Foale; Philip Smith (Comput. Games J., 2018)

Human-aligned artificial intelligence is a multiobjective problem. - Peter Vamplew 001; Richard Dazeley; Cameron Foale; Sally Firmin; Jane Mummery (Ethics Inf. Technol., 2018)

Non-functional regression: A new challenge for neural networks. - Peter Vamplew 001; Richard Dazeley; Cameron Foale; Tanveer A. Choudhury (Neurocomputing, 2018)

An Empirical Study of Reward Structures for Actor-Critic Reinforcement Learning in Air Combat Manoeuvring Simulation. - Budi Kurniawan; Peter Vamplew 001; Michael Papasimeon; Richard Dazeley; Cameron Foale (Australasian Conference on Artificial Intelligence, 2019)

Towards Machine Learning approach for Digital-Health intervention program. - Meena Santhanagopalan; Madhu Chetty; Cameron Foale; Britt Klein (Aust. J. Intell. Inf. Process. Syst., 2019)

A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions. - Peter Vamplew 001; Cameron Foale; Richard Dazeley (CoRR, 2020)

Discrete-to-Deep Supervised Policy Learning. - Budi Kurniawan; Peter Vamplew 001; Michael Papasimeon; Richard Dazeley; Cameron Foale (CoRR, 2020)

Language Representations for Generalization in Reinforcement Learning. - Goodger Nikolaj; Peter Vamplew 001; Cameron Foale; Richard Dazeley (ACML, 2021)

Levels of explainable artificial intelligence for human-aligned conversational explanations. - Richard Dazeley; Peter Vamplew 001; Cameron Foale; Charlotte Young; Sunil Aryal; Francisco Cruz 002 (Artif. Intell., 2021)

Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety. - Peter Vamplew 001; Cameron Foale; Richard Dazeley; Adam Bignold (Eng. Appl. Artif. Intell., 2021)

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)

Discrete-to-deep reinforcement learning methods. - Budi Kurniawan; Peter Vamplew 001; Michael Papasimeon; Richard Dazeley; Cameron Foale (Neural Comput. Appl., 2022)

The impact of environmental stochasticity on value-based multiobjective reinforcement learning. - Peter Vamplew 001; Cameron Foale; Richard Dazeley (Neural Comput. Appl., 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 conceptual framework for externally-influenced agents: an assisted reinforcement learning review. - Adam Bignold; Francisco Cruz 002; Matthew E. Taylor; Tim Brys; Richard Dazeley; Peter Vamplew 001; Cameron Foale (J. Ambient Intell. Humaniz. Comput., 2023)

Human engagement providing evaluative and informative advice for interactive reinforcement learning. - Adam Bignold; Francisco Cruz 002; Richard Dazeley; Peter Vamplew 001; Cameron Foale (Neural Comput. Appl., 2023)

Persistent rule-based interactive reinforcement learning. - Adam Bignold; Francisco Cruz 002; Richard Dazeley; Peter Vamplew 001; Cameron Foale (Neural Comput. Appl., 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)

Value function interference and greedy action selection in value-based multi-objective reinforcement learning. - Peter Vamplew 001; Cameron Foale; Richard Dazeley (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)

An empirical investigation of value-based multi-objective reinforcement learning for stochastic environments. - Kewen Ding; Peter Vamplew 001; Cameron Foale; Richard Dazeley (Knowl. Eng. Rev., 2025)