1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.AI2025★ 1 cited
A Scoresheet for Explainable AI
Michael Winikoff, John Thangarajah, Sebastian Rodriguez
Explainability is important for the transparency of autonomous and intelligent systems and for helping to support the development of appropriate levels of trust. There has been con…
cs.LG2022
SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning
Andrew Chester, Michael Dann, Fabio Zambetta +1
Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many inte…
cs.LG2021
Adapting to Reward Progressivity via Spectral Reinforcement Learning
Michael Dann, John Thangarajah
In this paper we consider reinforcement learning tasks with progressive rewards; that is, tasks where the rewards tend to increase in magnitude over time. We hypothesise that this…