[Lisa_seminaires] [Tea Talk] Bradly Stadie (UC Berkeley) Wed Feb 21 3:00PM AA1360

Michael Noukhovitch mnoukhov at gmail.com
Mar 20 Fév 12:14:57 EST 2018


Sorry for the lateness, please note this talk is a normal tea talk and it
is *tomorrow!* If you want to meet with the speaker, please email me and
I'll set it up!

This week we have *Bradly Stadie* from *UC Berkeley* giving a talk on
*Wednesday
Feb 21* at *3:00PM* in room *AA1360*.

See you there!
Michael

*TITLE* Towards Quickly Learning Agents

*KEYWORDS *Meta-learning, Reinforcement Learning, Imitation Learning


*ABSTRACT*We consider the problems of meta reinforcement learning, wherein
the goal is to train agents that can learn new tasks quickly. We suggest 5
major sub-problems that are crucial for enabling quickly learning agents:
Exploration, Imitation, Hierarchy/Planning, Credit Assignment, and Control.

The talk will examine the sub-problems of exploration and imitation and
their interplay with quickly learning agents in more depth. In particular,
we examine:

1) Third Person Imitation: which enables agents to imitate other agents
that differ significantly from themselves.
2) One Shot Imitation: which enables a robot to see a single video of a
demonstration and copy the demonstrated behavior.
3) E-MAML: a meta-RL algorithm that achieves significantly better
exploration than previous meta-RL algorithms.

*BIO*
Bradly Stadie is a PhD student at UC Berkeley in Pieter Abbeel’s lab.
Previously, he spent 2 years working at Open AI under Ilya Sutskever.
Bradly’s work has focused on the fields of exploration, imitation, and meta
learning with applications to games, robotics, and causal inference. Prior
to his interest in AI, Bradly studied mathematics at the University of
Chicago, where his work focused on representation theory of locally compact
groups and stochastic differential equations.
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