A Different Intro to RL in 30 Minutes
A 30 minute conceptual intro to Markov decision processes, iterative updates, and reinforcement learning.
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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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Nathan is a researcher, athlete, writer, and mental-health advocate.
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Reflecting on being a graduate student (in AI) in 2020
Starting to build my guide and advice for graduate school.
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AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks
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
We explored how MBRL can learn multi-step, nonlinear controllers!
Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Trying to reframe the MBRL framework with long-term predictions instead of one-step predictions!