Daniel Beechey
Research Scientist — Huawei Noah's Ark Lab, London
moc.srentrap-h@yehceeb.leinad
PhD Student — Bath RL Lab, University of Bath
ku.ca.htab@02bejd
I am a research scientist at the Huawei Noah's Ark Lab and a PhD student in the Bath Reinforcement Learning Lab, where I'm supervised by Özgür Şimşek. I'm interested in all things reinforcement learning, particularly questioning the basic building blocks we use to study and create artificial intelligence.
News
- Dec. 2025: Released Darwin Mobile Agent, the first fully open-source, end-to-end pipeline for large-scale online reinforcement learning and inference of mobile GUI agents. Paper + Code.
- Dec. 2025: Attended NeurIPS in San Diego.
- Sep. 2025: Gave a talk to the Edinburgh RL group. Slides.
- Sep. 2025: Paper on Approximating Shapley Explanations in Reinforcement Learning accepted as a poster at NeurIPS 2025! Poster.
- Jun. 2025: New preprint: A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values.
- Jun. 2025: Joined the AI Agent group at Huawei Noah's Ark Lab as a Research Scientist!
- Jun. 2025: Presented a poster at the 2025 Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM). Poster.
- Feb. 2025: Gave a talk as part of the ART-AI colloquium series. Slides.
- Oct. 2024: Presenting a poster at the 17th European Workshop on Reinforcement Learning (EWRL). Poster.
- Sep. 2024: Attended the Mediterranean Machine Learning Summer School. Poster.
- Jul. 2024: Attended the CIFAR Deep Learning + Reinforcement Learning (DLRL) Summer School. Poster.
- Jun. 2024: Accepted a Doctoral Recognition Award at the Bath Doctoral Research Celebration Evening!
- Jun. 2024: Gave a talk as part of the Digital Futures session at the Bath Doctoral Festival of Ideas. Poster.
- Apr. 2024: Gave a talk to the Bath AI Society on the work of the Bath Reinforcement Learning Lab.
- Oct. 2023: Attended the UKRI Inter AI CDT Conference in Bristol. Poster.
- Sep. 2023: Our work on "Reformulating Reactivity Design for Data-Efficient Machine Learning" has been accepted to ACS Catalysis!
- Aug. 2023: Presented our poster on "Explaining Reinforcement Learning with Shapley Values" at ICML 2023.
- Jul. 2023: Presented a talk on "Explaining Reinforcement Learning with Shapley Values" at the Bath Conference of Computer Science.
- Jun. 2023: Presented a talk on "Explaining Reinforcement Learning with Shapley Values" at the Alan Turing Institute Student Presentations.
- Apr. 2023: Our paper on "Explaining Reinforcement Learning with Shapley Values" has been accepted as a poster at ICML 2023!
- Oct. 2021: Joined the Bath Reinforcement Learning Lab at the University of Bath as a 1st year Ph.D. student.
Research Interests
Throughout my PhD, my interests centred on identifying and explaining how agents interact with their environments, specifically their behaviour, outcomes, and predictions. Recently, I have been interested in bounded rationality and how core elements of intelligence, such as representations, hierarchy, and continual learning, naturally emerge when agents operate with limited resources. This has led me to question the foundations of reinforcement learning, examining how our basic assumptions about agents and environments influence the algorithms and conceptual models we design.
Papers
Talks
Explaining Reinforcement Learning with Shapley Values: Theory and Algorithms
Edinburgh RL Group, September 2025
A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values
ART-AI Colloquium Series, February 2025
Bath Doctoral Festival of Ideas, July 2024
An Introduction to Explainable and Hierarchical Reinforcement Learning
Bath AI Society, April 2024
Explaining Reinforcement Learning with Shapley Values
Bath Computer Science Conference, July 2023
Alan Turing Institute Student Presentations, June 2023
Teaching
- Lecturer — University of Bath
- Reinforcement Learning, 2023 [slides][slides]
- Reinforcement Learning (Online), 2023
- Teaching Assistant — University of Bath
- Reinforcement Learning, 2022–2023
- Software Technologies for Data Science, 2021
- Statistics for Data Science, 2021
- Programming, Foundations, and Connections, 2021
- Programming and Discrete Mathematics, 2021
- Mathematical Methods and Applications, 2020