5 citations · 11 across the 7 of their papers we have counts for
3 papers · 1 filter
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…
Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning
Joseph Early, Tom Bewley, Christine Evers +1
We generalise the problem of reward modelling (RM) for reinforcement learning (RL) to handle non-Markovian rewards. Existing work assumes that human evaluators observe each step in…
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…