Debugging Deep Model-based Reinforcement Learning Systems
Things I have learned in 3 years of a young, and generally tricky research field.
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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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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.
Read more...A Different Intro to RL in 30 Minutes
A 30 minute conceptual intro to Markov decision processes, iterative updates, and reinforcement learning.
Read more...Papers
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.
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!