Search Results for author: Michael Luo

Found 10 papers, 6 papers with code

Accelerating Quadratic Optimization with Reinforcement Learning

1 code implementation NeurIPS 2021 Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato, Goran Banjac, Michael Luo, Francesco Borrelli, Joseph E. Gonzalez, Ion Stoica, Ken Goldberg

First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.

reinforcement-learning

LazyDAgger: Reducing Context Switching in Interactive Imitation Learning

no code implementations31 Mar 2021 Ryan Hoque, Ashwin Balakrishna, Carl Putterman, Michael Luo, Daniel S. Brown, Daniel Seita, Brijen Thananjeyan, Ellen Novoseller, Ken Goldberg

Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired behavior.

Continuous Control Imitation Learning

Connecting Context-specific Adaptation in Humans to Meta-learning

no code implementations27 Nov 2020 Rachit Dubey, Erin Grant, Michael Luo, Karthik Narasimhan, Thomas Griffiths

This work connects the context-sensitive nature of cognitive control to a method for meta-learning with context-conditioned adaptation.

Meta-Learning

RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem

1 code implementation NeurIPS 2021 Eric Liang, Zhanghao Wu, Michael Luo, Sven Mika, Joseph E. Gonzalez, Ion Stoica

Researchers and practitioners in the field of reinforcement learning (RL) frequently leverage parallel computation, which has led to a plethora of new algorithms and systems in the last few years.

reinforcement-learning

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

2 code implementations29 Oct 2020 Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, Ken Goldberg

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.

reinforcement-learning Safe Reinforcement Learning

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