116 citations · 272 across the 19 of their papers we have counts for
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What Really Matters in Matrix-Whitening Optimizers?
Kevin Frans, Pieter Abbeel, Sergey Levine
A range of recent optimizers have emerged that approximate the same "matrix-whitening" transformation in various ways. In this work, we systematically deconstruct such optimizers,…
A Stable Whitening Optimizer for Efficient Neural Network Training
Kevin Frans, Sergey Levine, Pieter Abbeel
In this work, we take an experimentally grounded look at neural network optimization. Building on the Shampoo family of algorithms, we identify and alleviate three key issues, resu…
Horizon Reduction Makes RL Scalable
Seohong Park, Kevin Frans, Deepinder Mann +3
In this work, we study the scalability of offline reinforcement learning (RL) algorithms. In principle, a truly scalable offline RL algorithm should be able to solve any given prob…
Diffusion Guidance Is a Controllable Policy Improvement Operator
Kevin Frans, Seohong Park, Pieter Abbeel +1
At the core of reinforcement learning is the idea of learning beyond the performance in the data. However, scaling such systems has proven notoriously tricky. In contrast, techniqu…