6 citations · 6 across the 4 of their papers we have counts for
5 papers
Subgoal-aware Memory Graph for GUI Agent
Dezhao Luo, Daniel Beechey, Derek Yuen +1
GUI agents aim to automate complex user tasks by interacting with mobile interfaces, but ef- fective decision-making in long-horizon set- tings requires reusing prior experience ra…
Darwin Mobile Agent: A Roadmap for Self-Evolution
Daniel Beechey, Derek Yuen, Jianheng Liu +5
The goal of artificial intelligence is to create agents capable of general, adaptive behaviour in open-ended environments. Guided by the "Bitter Lesson", we argue that the most eff…
Approximating Shapley Explanations in Reinforcement Learning
Daniel Beechey, Özgür Şimşek
Reinforcement learning has achieved remarkable success in complex decision-making environments, yet its lack of transparency limits its deployment in practice, especially in safety…
A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values
Daniel Beechey, Thomas M. S. Smith, Özgür Şimşek
Reinforcement learning agents can achieve super-human performance in complex decision-making tasks, but their behaviour is often difficult to understand and explain. This lack of e…
Explaining Reinforcement Learning with Shapley Values
Daniel Beechey, Thomas M. S. Smith, Özgür Şimşek
For reinforcement learning systems to be widely adopted, their users must understand and trust them. We present a theoretical analysis of explaining reinforcement learning using Sh…