Connectionists: NIPS 2018 Workshop — Infer2Control — Call for Papers

[Roy Fox]

We invite all researchers to submit their manuscripts for review.

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> Infer to Control: Probabilistic Reinforcement Learning and Structured
Control
> NIPS 2018 Workshop
> Saturday, December 8
> Montréal, Canada
> Website: https://sites.google.com/view/infer2control-nips2018
> Please address questions to: infer2control.nips2018 at gmail.com
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Reinforcement learning and imitation learning are effective paradigms for
learning controllers of dynamical systems from experience. These fields
have been empowered by recent success in deep learning of differentiable
parametric models, allowing end-to-end training of highly nonlinear
controllers that encompass perception, memory, prediction, and decision
making. The aptitude of these models to represent latent dynamics,
high-level goals, and long-term outcomes is unfortunately curbed by the
poor sample complexity of many current algorithms for learning these models
from experience.

Probabilistic reinforcement learning and inference of control structure are
emerging as promising approaches for avoiding prohibitive amounts of
controller–system interactions. These methods leverage informative priors
on useful behavior, as well as controller structure, such as hierarchy and
modularity, as useful inductive biases that reduce the effective size of
policy search space and shape the optimization landscape. Intrinsic and
self-supervised signals can further guide the training process of distinct
internal components—such as perceptual embeddings, predictive models,
exploration policies, and inter-agent communication—to break down the hard
holistic problem of control into more efficiently learnable parts.

Effective inference methods are crucial for probabilistic approaches to
reinforcement learning and structured control. Approximate control and
model-free reinforcement learning exploit latent system structure and
priors on policy structure, that are not directly evident in the
controller–system interactions, and must be inferred by the learning
algorithm. The growing interest of the reinforcement learning and optimal
control community in the application of inference methods is synchronized
well with the development by the probabilistic learning community of
powerful inference techniques, such as probabilistic programming,
variational inference, Gaussian processes, and nonparametric regression.

This workshop is a venue for the inference and reinforcement learning
communities to come together in discussing recent advances, developing
insights, and future potential in inference methods and their application
to probabilistic reinforcement learning and structured control. The goal of
this workshop is to catalyze tighter collaboration within and between the
communities, that will be leveraged in upcoming years to rise to the
challenges of real-world control problems.

#### IMPORTANT DATES: ####
- Submission deadline: Friday, October 5, 2018 (Anywhere on Earth)
- Author notification: Monday, October 22, 2018
- Final version deadline: Friday, November 30, 2018
- Workshop: Saturday, December 8, 2018

#### SUBMISSION DETAILS: ####
- Research papers are solicited on inference for reinforcement learning and
control, its theory and applications, and related fields.
- Contributed papers may include novel research, preliminary results, or
surveys of recent results.
- Papers are limited to 4 pages, excluding references, in the NIPS style:
https://nips.cc/Conferences/2018/PaperInformation/StyleFiles.
- Submissions must be anonymized for double-blind review.
- All accepted papers will be presented as spotlights and posters, and made
publicly available as a non-archival report, allowing future submissions to
archival conferences or journals.
- Authors of top accepted papers will be invited to give short contributed
talks.
- Submission link: https://cmt3.research.microsoft.com/INFER2CONTROL2018
- Please check the workshop website for the latest updates:
https://sites.google.com/view/infer2control-nips2018

#### ORGANIZERS: ####
Leslie Kaelbling
Martin Riedmiller
Marc Toussaint
Igor Mordatch
Roy Fox
Tuomas Haarnoja
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