David Abel



I am a Senior Research Scientist at DeepMind in London. Before that, I completed my Ph.D in Computer Science and Masters in Philosophy at Brown University, where I was fortunate to be advised by Prof. Michael Littman (CS), and Prof. Joshua Schechter (Philosophy).



My research focuses on bringing clarity to the central philosophical questions surrounding computation and learning.

I value research that concentrates on providing new understanding, and tend to get excited by simple but foundational questions. I typically work with the reinforcement learning problem, drawing on tools and perspectives from computational learning theory, computational complexity, and analytic philosophy.

I am currently interested in better defining the main concepts of AI, such as learning, agency, and goals. Previously, my dissertation studied how agents model the worlds they inhabit, focusing on the representational practices that underly effective learning and planning.

Featured Research

Planned Information Processing

We develop a new theory describing how people simplify and represent problems when planning.

We illustrate the implicit requirements on goals and purposes under which the reward hypothesis holds.

Led by Michael Bowling and John D. Martin, joint with Will Dabney.

Alice, Bob, and RL
On the Expressivity of Markov Reward
NeurIPS 2021 (Outstanding Paper Award)

We study the expressivity of Markov reward functions in finite environments by analysing what kinds of tasks such functions can express.

Thesis overview

My dissertation, aimed at understanding abstraction and its role in effective reinforcement learning.

Advised by Michael L. Littman.

Value Preserving Abstractions

We prove which combinations of state abstractions and options are guaranteed to preserve representation of near-optimal policies in any finite Markov Decision Process.

The process of abstraction
The Value of Abstraction
Current Opinions in Behavioral Science 2019

We discuss the vital role that abstraction plays in efficient decision making.

Point Options

We prove that the problem of finding options that minimize planning time is NP-Hard.

Selected Awards

About Me

I'm a big fan of basketball, reading, lifting, immersive theater, games, snowboarding, and music (I play guitar/piano/violin and love listening to just about everything). I live in London, UK, with my wife Elizabeth and our dog Barley.

Always up for a chat -- shoot me an email if you'd like to discuss anything! If you would like to arrange a call, I have a recurring open slot in my calendar here.

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