1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.AI2023
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback
Tom Bewley, Jonathan Lawry, Arthur Richards
We propose a method to capture the handling abilities of fast jet pilots in a software model via reinforcement learning (RL) from human preference feedback. We use pairwise prefere…
cs.LG2022
Reward Learning with Trees: Methods and Evaluation
Tom Bewley, Jonathan Lawry, Arthur Richards +2
Recent efforts to learn reward functions from human feedback have tended to use deep neural networks, whose lack of transparency hampers our ability to explain agent behaviour or v…
cs.AI2022★ 1 cited
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction
Tom Bewley, Jonathan Lawry, Arthur Richards
We introduce a data-driven, model-agnostic technique for generating a human-interpretable summary of the salient points of contrast within an evolving dynamical system, such as the…