Alexander D. Goldie

Alexander D. Goldie

I am a PhD student at the University of Oxford, under the supervision of Jakob Foerster and Shimon Whiteson. My research focuses on automated algorithm discovery, autonomous AI research and meta-reinforcement learning.

Currently, I am a Research Scientist Intern at Wayve, where I have been working on offline reinforcement learning for autonomous driving. My PhD is offered by AIMS, a competitive PhD-level course for Machine Learning.

Email / CV / Scholar / Twitter / Github / LinkedIn

News

(12/25) We released a blog for DiscoBench!
(08/25) I was invited to talk on two episodes of TalkRL.
(08/25) My paper was awarded "Outstanding Paper for Scientific Understanding In Reinforcement Learning" at RLC 2025!
(06/25) I started a Research Scientist internship at Wayve.
(12/24) OPEN was awarded a Spotlight at NeurIPS 2024!
(07/24) I took part in a panel discussion at the AutoRL workshop in ICML24.
(06/24) OPEN was awarded a Spotlight at the AutoRL workshop in ICML24.
(10/22) I started my PhD on AIMS at the University of Oxford.
(07/22) I graduated from my MEng Engineering Science at the University of Oxford with a high First!

Research

	DiscoBench: An Open-Ended Benchmark For Algorithm Discovery Alexander D. Goldie, Zilin Wang, Adrian Hayler, Deepak Nathani, Edan Toledo, Ken Thampiratwong, Aleksandra Kalisz, Michael Beukman, Alistair Letcher, Shashank Reddy, Clarisse Wibault, Theo Wolf, Charles O'Neill, Shimon Whiteson, Jakob N. Foerster, Roberta Raileanu blog \| code
	How Should We Meta-Learn Reinforcement Learning Algorithms? Alexander D. Goldie, Zilin Wang, Jaron Cohen, Jakob N. Foerster, Shimon Whiteson RL Conference 2025 (Outstanding Paper) arXiv \| code \| tweet
	An Optimisation Framework For Unsupervised Environment Design Nathan Monette, Alistair Letcher, Michael Beukman, Matthew T. Jackson, Alexander Rutherford, Alexander D. Goldie, Jakob N. Foerster RL Conference 2025 arXiv \| code \| site \| tweet
	Can Learned Optimization Make Reinforcement Learning Less Difficult? Alexander D. Goldie, Chris Lu, Matthew T. Jackson, Shimon Whiteson, Jakob N. Foerster NeurIPS 2024 (Spotlight Award) Also at AutoRL Workshop @ ICML2024 (Spotlight) arXiv \| code \| tweet
	Adam On Local Time: Addressing Nonstationarity In RL With Relative Adam Timesteps Benjamin Ellis, Matthew T. Jackson, Andrei Lupu, Alexander D. Goldie, Mattie Fellow, Shimon Whiteson, Jakob N. Foerster NeurIPS 2024 paper
	Robust Offline Learning via Adversarial World Models Uljad Berdica, Kelvin Li, Michael Beukman, Alexander D. Goldie, Mattie Fellows, Perla Maiolino, Jakob N. Foerster Open-World Agents Workshop & Adversarial ML Workshop @ NeurIPS24 paper

Website template by Jon Barron and inspired by Chris Lu.

News

Research

DiscoBench: An Open-Ended Benchmark For Algorithm Discovery

How Should We Meta-Learn Reinforcement Learning Algorithms?

An Optimisation Framework For Unsupervised Environment Design

Can Learned Optimization Make Reinforcement Learning Less Difficult?

Adam On Local Time: Addressing Nonstationarity In RL With Relative Adam Timesteps

Robust Offline Learning via Adversarial World Models