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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
- Chelsea Finn, P. Abbeel, S. Levine
- Computer ScienceICML
- 9 March 2017
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning…
End-to-End Training of Deep Visuomotor Policies
- S. Levine, Chelsea Finn, Trevor Darrell, P. Abbeel
- Computer ScienceJ. Mach. Learn. Res.
- 2 April 2015
TLDR
Unsupervised Learning for Physical Interaction through Video Prediction
- Chelsea Finn, Ian J. Goodfellow, S. Levine
- Computer ScienceNIPS
- 23 May 2016
TLDR
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
- Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, S. Levine
- Computer ScienceICML
- 19 March 2019
TLDR
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
- Chelsea Finn, S. Levine, P. Abbeel
- Computer ScienceICML
- 1 March 2016
TLDR
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
- Tianhe Yu, Deirdre Quillen, S. Levine
- Computer ScienceCoRL
- 24 October 2019
TLDR
Gradient Surgery for Multi-Task Learning
- Tianhe Yu, Saurabh Kumar, Abhishek Gupta, S. Levine, Karol Hausman, Chelsea Finn
- Computer ScienceNeurIPS
- 19 January 2020
TLDR
MOPO: Model-based Offline Policy Optimization
TLDR
Stochastic Variational Video Prediction
- M. Babaeizadeh, Chelsea Finn, D. Erhan, R. Campbell, S. Levine
- Computer ScienceICLR
- 30 October 2017
TLDR
Meta-Learning with Implicit Gradients
- A. Rajeswaran, Chelsea Finn, S. Kakade, S. Levine
- Computer ScienceNeurIPS
- 10 September 2019
TLDR
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