(Georgia Institute of Technology, 2018-08-20)
Abbeel, Pieter
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.