Adam Bignold
Adam Bignold
Federation Univeristy Australia
Verified email at federation.edu.au - Homepage
Title
Cited by
Cited by
Year
Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety
P Vamplew, C Foale, R Dazeley, A Bignold
Engineering Applications of Artificial Intelligence 100, 104186, 2021
42021
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
A Bignold, F Cruz, R Dazeley, P Vamplew, C Foale
arXiv preprint arXiv:2009.09575, 2020
22020
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
A Bignold, F Cruz, ME Taylor, T Brys, R Dazeley, P Vamplew, C Foale
arXiv preprint arXiv:2007.01544, 2020
22020
An Evaluation Methodology for Interactive Reinforcement Learning with Simulated Users
A Bignold, F Cruz, R Dazeley, P Vamplew, C Foale
Biomimetics 6 (1), 13, 2021
12021
Persistent Rule-based Interactive Reinforcement Learning
A Bignold, F Cruz, R Dazeley, P Vamplew, C Foale
arXiv preprint arXiv:2102.02441, 2021
12021
Rule-Based Interactive Assisted Reinforcement Learning
A Bignold, P Vamplew, R Dazeley, C Foale
https://www.researchgate.net/publication/337404428_Rule …, 2019
12019
Supporting Regional Aged Care Nursing Staff to Manage Residents' Behavioural and Psychological Symptoms of Dementia, in Real Time, Using the Nurses' Behavioural Assistant (NBA …
B Klein, L Clinnick, J Chesler, A Stranieri, A Bignold, R Dazeley, ...
Telehealth for our Ageing Society, 24-28, 2018
2018
Coarse Q-Learning: Addressing the convergence problem when quantizing continuous state variables
R Dazeley, P Vamplew, A Bignold
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