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.

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A Different Intro to RL in 30 Minutes

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


Lifelong Learning 2021

What I have been learning from recently.


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

Starting to build my guide and advice for graduate school.

More musings →


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

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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

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

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

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