Nathan is a robot learning researcher at UC Berkeley, writer, non-professional athlete, and a mental-health advocate.

There really is too much noise.

I do my best to only contribute high signal content on machine learning, human optimization, and the nature of life.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

Blogos

A Different Intro to RL in 30 Minutes

A 30 minute conceptual intro to Markov decision processes, iterative updates, and reinforcement learning.

Read more...

Lifelong Learning 2021

What I have been learning from recently.

Read more...

Reflecting on being a graduate student (in AI) in 2020

Starting to build my guide and advice for graduate school.

Read more...
More musings →

Papers

AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks

Learn more.
We study three developing subfields of AI research and their growing relationship with the sociotechnical: AI Safety, Fair Machine Learning, and Human-in-the-loop Autonomy.

Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning

Learn more.
We explored how MBRL can learn multi-step, nonlinear controllers!

Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning

Learn more.
Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!
More papers →