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
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.