Reinforcement Learning for Real Life

ICML 2019 Workshop

June 14, 2019, Long Beach, CA, USA

Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm. RL provides solution methods for sequential decision making problems as well as those can be transformed into sequential ones. RL connects deeply with optimization, statistics, game theory, causal inference, sequential experimentation, etc., overlaps largely with approximate dynamic programming and optimal control, and applies broadly in science, engineering and arts.

RL has been making steady progress in academia recently, e.g., Atari games, AlphaGo, visuomotor policies for robots. RL has also been applied to real world scenarios like recommender systems and neural architecture search. See a recent collection about RL applications. It is desirable to have RL systems that work in the real world with real benefits. However, there are many issues for RL though, e.g. generalization, sample efficiency, and exploration vs. exploitation dilemma. Consequently, RL is far from being widely deployed. Common, critical and pressing questions for the RL community are then: Will RL have wide deployments? What are the issues? How to solve them?

The goal of this workshop is to bring together researchers and practitioners from industry and academia interested in addressing practical and/or theoretical issues in applying RL to real life scenarios, review state of the arts, clarify impactful research problems, brainstorm open challenges, share first-hand lessons and experiences from real life deployments, summarize what has worked and what has not, collect tips for people from industry looking to apply RL and RL experts interested in applying their methods to real domains, identify potential opportunities, generate new ideas for future lines of research and development, and promote awareness and collaboration. This is not "yet another RL workshop": it is about how to successfully apply RL to real life applications. This is a less addressed issue in the RL/ML/AI community, and calls for immediate attention for sustainable prosperity of RL research and development.

Call For Paper

The main goals of the workshop are to: (1) have experts share their successful stories of applying RL to real-world problems; and (2) identify research sub-areas critical for real-world applications such as reliable evaluation, benchmarking, and safety/robustness.

We invite paper submissions successfully applying RL and relevant algorithms to real life RL applications by addressing relevant RL issues. Under the central theme of making RL work in real life scenarios, no further constraints are set, to facilitate open discussions and to foster the most potential creativity and imagination from the community. We will prioritize work that propose interesting and impactful contributions. Our technical topics of interest are general, including but not limited to concrete topics below:

  • RL and relevant algorithms: value-based, policy-based, model-free, model-based, online, offline, on-policy, off-policy, hierarchical, multi-agent, relational, multi-armed bandit, (linear, nonlinear, deep/neural, symbolic) representation learning, unsupervised learning, self-supervised learning, transfer learning, sim-to-real, multi-task learning, meta-learning, imitation learning, continual learning, causal inference, and reasoning;
  • Issues: generalization, deadly triad, sample/time/space efficiency, exploration vs. exploitation, reward specification, stability, convergence, scalability, model-based learning (model validation and model error estimation), prior knowledge, safety, interpretability, reproducibility, hyper-parameters tuning, and boilerplate code;
  • Applications: recommender systems, advertisements, conversational AI, business, finance, healthcare, education, robotics, autonomous driving, transportation, energy, chemical synthesis, drug design, industry control, drawing, music, and other problems in science, engineering and arts.

We warmly welcome position papers.

We invite unpublished submissions up to 8 pages excluding references, in PDF format using the ICML 2019 template and style guidelines. We are open to papers currently under review at other venues. Submission is single-blind. All accepted papers will be presented as posters, and a few of them will be selected for spotlight presentations. There will be no proceedings for this workshop. However, accepted contributions will be made available on the workshop website, unless authors opt out. The submission website is: https://openreview.net/group?id=ICML.cc/2019/Workshop/RL4RealLife.

Important dates:

  • Submission deadline: May 1, May 5, 2019 (23:59 EST)
  • Author notification: May 15, May 25, 2019 (Please start trip arrangement earlier. Our workshop would be great. ICML main conference would be great for sure.)
  • Final submission: May 30, June 3, 2019

Info for Posters:

All posters will be presented inside the room of the workshop. There are no poster boards at workshops. Posters are taped to the wall. Posters should be on light weight paper, not laminated. Please make posters 36W x 48H inches or 90 x 122 cm.

Final version:

Style files for our workshop (customized from that of ICML 2019).

Instruction: Based on ICML 2019 submission style files, 1) change \usepackage{icml2019} to \usepackage[accepted]{icml2019} in your .tex file for the final style; and 2) use our customized icml2019.sty file for the foot note of our workshop.

Schedule: TBA

Speakers / Panelists

  • Pieter Abbeel (Berkeley, covariant.ai)
  • Craig Boutilier (Google Research)
  • Emma Brunskill (Stanford)
  • John Langford (Microsoft Research)
  • David Silver (Deepmind)
  • David Sontag (MIT)

Organizers

Email: rl4reallife@gmail.com LinkedIn Group Twitter: #RL4RealLife