[Lisa_seminaires] [Tea Talk] Dzmitry Bahadanau (MILA) Fri June 22 10:30AM AA3195

Michael Noukhovitch mnoukhov at gmail.com
Lun 18 Juin 13:10:47 EDT 2018


This week we have our very own *Dzmitry Bahadanau* giving a talk on work he
did on internship at DeepMind on *Friday June 22* at *10:30AM* in room
*AA3195*.

Help Dima get a human baseline for instruction following and *come to the
tea talk*
Michael

*TITLE* Learning to Follow Language Instructions with Adversarial Reward
Induction

*KEYWORDS *language understanding, reinforcement learning, grounded language


*ABSTRACT*
Recent work has shown that deep reinforcement-learning agents can learn to
follow language-like instructions from infrequent environment rewards.
However, for many real-world natural language commands that involve a
degree of underspecification or ambiguity, such as *tidy the room*, it
would be challenging or impossible to program an appropriate reward
function. To overcome this, we present a method for learning to follow
commands from a training set of instructions and corresponding example
goal-states, rather than an explicit reward function. Importantly, the
example goal-states are not seen at test time. The approach effectively
separates the representation of what instructions require from how they can
be executed. In a simple grid world, the method enables an agent to learn a
range of commands requiring interaction with blocks and understanding of
spatial relations and underspecified abstract arrangements. We further show
the method allows our agent to adapt to unseen instructions and changes in
the environment without requiring new training examples.

*BIO*
Dzmitry Bahdanau is a Ph.D. student at the Montreal Institute for Learning
Algorithms under the supervision of Yoshua Bengio.  His research interest
is enabling intelligent assistants that could collaborate with humans and
communicate with them in natural language. In the view of this long-term
research goal he is interested in language acquisition, language grounding
models, reinforcement learning and imitation learning. Dzmitry studied
applied mathematics at Belarusian State University in Minsk and obtained
his MsC degree in computer science at Jacobs University Bremen, Germany.
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