[Lisa_seminaires] [Tea Talk] Romain Laroche (MS Maluuba) Fri Dec 1 10:30AM, AA6214

Michael Noukhovitch mnoukhov at gmail.com
Ven 1 Déc 09:39:16 EST 2017


Reminder: this is in 1 hour!

On Mon, Nov 27, 2017, 08:29 Michael Noukhovitch, <mnoukhov at gmail.com> wrote:

> This week we have a researcher from MS Maluuba, *Romain Laroche,* giving
> a talk on *Friday Dec 1* at* 10:30AM* in room *AA6214*.
>
> See you there, this RL talk is sure to be rewarding!
> Michael
>
> *KEYWORDS* policy-based RL, bootstrapping, data/computational efficiency
>
> *TITLE *Safe Policy Improvement with Baseline Bootstrapping
>
> *ABSTRACT*
>
> A common goal in Reinforcement Learning is to derive a good strategy given
> a limited batch of data. In this paper, we propose a new strategy to
> compute a safe policy, guaranteed to perform at least as well as a given
> baseline strategy. We advocate that the assumptions made in previous work
> are too strong for real world applications and propose new algorithms
> allowing those assumptions to be satisfied only in a subset of the
> state-action pairs. While significantly relaxing the assumptions, our
> algorithms achieve the same accuracy guarantees than the previous work, and
> are also much more computationally efficient. We also show that the
> algorithms can be adapted to model-free Reinforcement Learning.
>
>
> *BIO*
>
> Graduated from Ecole Polytechnique in 2001 and then from Telecom PariTech
> in 2003, Romain Laroche joined the dialogue team at Orange in Paris, where
> he defended in 2010 a corporate PhD on Reinforcement Learning for
> industrial dialogue systems at Université Pierre et Marie Curie (Paris VI).
>
>
> Romain joined Maluuba in 2016, and is now a researcher at Microsoft
> Research Maluuba. During the past 6 years he supervised 5 PhDs, 3 postdocs,
> and a dozen of undergrad interns. Counting more than 40 papers at
> international conferences, his interest focuses now on Reinforcement
> Learning. His preferred application domains are dialogue systems (still his
> primary real-world motivation), Atari games (for benchmarking against other
> algorithms), and navigation toy problems (for empirical analysis and
> algorithm design).
>
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