Deep Learning to Learn - Pieter Abbeel

From Katie Gentilello August 24th, 2018

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Reinforcement learning and imitation learning have seen success in many domains, including autonomous helicopter flight, Atari, simulated locomotion, Go, robotic manipulation. However, sample complexity of these methods remains very high. In contrast, humans can pick up new skills far more quickly. To do so, humans might rely on a better learning algorithm or on a better prior (potentially learned from past experience), and likely on both. In this talk I will describe some recent work on meta-learning for action, where agents learn the imitation/reinforcement learning algorithms and learn the prior. This has enabled acquiring new skills from just a single demonstration or just a few trials. While designed for imitation and RL, our work is more generally applicable and also advanced the state of the art in standard few-shot classification benchmarks such as omniglot and mini-imagenet.

Tags: irim seminar seriesml@gt seminar seriesgeorgia techrecorded by gtlibraryfall 2018

name: Pieter Abbeel
Date: August 20th, 2018
Appears In: Open Scholarship

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Deep Learning to Learn - Pieter Abbeel

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