Nathan is a robot learning researcher, 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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Debugging Deep Model-based Reinforcement Learning Systems

Things I have learned in 3 years of a young, and generally tricky research field.


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

Starting to build my guide and advice for graduate school.


A Different Intro to RL in 30 Minutes

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

More musings →


Axes for Sociotechnical Inquiry in AI Research

We present a concise set of directions for understanding the societal risks of new directions of AI research.

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

An open-source PyTorch repository designed from the bottom up for model-based reinforcement learning research.

On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning

We showed that advancements in AutoML when paired with common deep RL tasks, MBRL algorithms perform so well they break the simulator.
More papers →