Call for Papers: Reinforcement Learning for Real Life (2nd Issue)

Guest Editors:
Minmin Chen (Google)
Omer Gottesman (Brown U.)
Lihong Li (Amazon)
Yuxi Li (Attain.ai)
Zongqing Lu (PKU)
Rupam Mahmood (U. of Alberta)
Niranjani Prasad (Microsoft)
Zhiwei (Tony) Qin (Didi)
Csaba Szepesvari (Deepmind & U. of Alberta)
Matthew E. Taylor (U. of Alberta)

Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm
and applies broadly in many disciplines in science, engineering and arts. RL has seen
prominent successes in many problems, such as Atari games, AlphaGo, robotics, recommender
systems, and AutoML. However, applying RL in the real world remains challenging, and a
natural question is:
Why isn’t RL used even more often and how can we improve this?
The main goals of the workshop are to: (1) identify key research problems that are critical for the
success of real-world applications; (2) report progress on addressing these critical issues; and
(3) have practitioners share their success stories of applying RL to real-world problems, and the
insights gained from such applications.

We invite paper submissions successfully applying RL algorithms to real-life problems
and/or addressing practically relevant RL issues. Our topics of interest are general,
including (but not limited to):

  • Practical RL algorithms, which cover all algorithmic challenges of RL, especially those that directly address challenges faced by real-world applications;
  • Practical issues: model-based RL, sim2real, batch/offline learning, off-policy estimation, pre-training, representation learning, generalization, sample/time/space efficiency, exploration, reward specification and shaping, multi-objective, scalability, prior knowledge, safety, accountability, interpretability, reproducibility, hyper-parameter tuning, software engineering, production deployment, post-deployment, real-time learning/adaptation, etc.;
  • Applications: advertising, autonomous driving, business processes, chemical synthesis, computer systems, conversational AI, drawing, drug design, education, energy, finance, healthcare, industrial control, music, recommender systems, robotics, transportation, or other problems in science, engineering and humanities.

We require that each submission contains study about real life issues. We welcome both
application- and algorithm/theory-oriented submissions. For application-oriented ones, we are particularly interested in those investigating issues in real life deployment/production. For an
algorithm/theory-oriented submission, we require the investigation with real life experiments.

Submission Info
Submissions should be made via the Machine Learning journal website at
http://www.editorialmanager.com/mach/. When submitting your paper, be sure to specify that the
paper is a contribution for the Special Issue “SI: Reinforcement Learning For Real Life” so
that your paper will be assigned to the guest editors.

It is the policy of the journal that no submission, or substantially overlapping submission, be
published or be under review at another journal or conference at any time during the review
process. Papers extending previously published conference papers are acceptable, as long as
the journal submission provides a significant contribution beyond the conference paper, and the overlap is described clearly at the beginning of the journal submission. If you have any question about whether the overlap with another paper is “substantial,” please include in the paper a discussion of the similarities and differences with other papers, including the unique
contribution(s) of the Machine Learning submission.
Springer does not require authors to submit their papers in a prescribed template. If the paper is
accepted for publication the source files will be converted by the typesetter and prepared in
Springer’s format for the online platform, SpringerLink. Accepted papers will be published
online, before print publication. Resources for journal authors, including templates and style
files, as well as frequently asked questions can be found at: Journal Author Resources,
http://www.springer.com/gp/authors-editors/journal-author/frequently-asked-questions/3832, in particular, Submission Guidelines for Machine Learning Journal:
https://www.springer.com/journal/10994/submission-guidelines.

For any inquiry about the special issue, please contact us at RL4RealLife2021@gmail.com. We
are looking forward to receiving your contribution.

Editorial Schedule:
Submission deadline: 02/15/2022
1st Review: 04/30/2022
Revise and resubmit: 07/15/2022

2nd Review: 08/30/2022
Decision: 09/15/2022

In case 2nd revision:
2nd Revision: 10/15/2022
3rd Review: 12/30/2022
Decision: 01/15/2023