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)

Broad-persistent Advice for Interactive Reinforcement Learning Scenarios. - Francisco Cruz 002; Adam Bignold; Hung Son Nguyen; Richard Dazeley; Peter Vamplew 001 (CoRR, 2022)

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)