2020 Virtual Conference on Reinforcement Learning for Real Life

June 27-28, 2020

Youtube streaming for RL + healthcare panel discussion:

https://youtu.be/dDSENm2smkQ

Video for RL general topic panel discussion:

https://youtu.be/lDdC8Gjat9w

Poster info spreadsheet (see also list of papers below)

Welcome to join Slack for RL for Real Life for discussions.

Dates/Times

Atlantic Run: San Francisco (SF) 9am-12pm, June 27

Pacific Run: SF 6-9pm, June 27

Schedule

Hours 0:00-1:00 Live panel discussion / Q&A

Hours 1:00-3:00 Online poster sessions

Invited Speakers / Panelists

We will request invited speakers and moderators to share their expertise w.r.t. the real life aspects of RL by pre-recording videos. The moderators will host a live panel discussion at the listed times, and there will be polls for audience members to submit their questions during these live panel discussions.


RL+healthcare, SF 9-10am, June 27

Youtube streaming: https://youtu.be/dDSENm2smkQ

RL general topic, SF 6-7pm, June 27

Youtube streaming: https://youtu.be/lDdC8Gjat9w

Call For Papers

Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm that applies broadly 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, so a natural question is:

What are the challenges of applying RL in the real world, and how can we solve them?

The main goals of the conference 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 the applications.


We invite you to submit papers that successfully apply RL algorithms to real-life problems by addressing practically relevant RL issues. Our topics of interest are general, including but not limited to topics below:

  • Practical RL algorithms, which covers all algorithmic challenges of RL, especially those that directly address challenges faced by real-world applications;

  • Practical issues: generalization, sample/time/space efficiency, exploration vs. exploitation, reward specification and shaping, scalability, model-based learning (model validation and model error estimation), incorporating prior knowledge, safety, accountability, interpretability, reproducibility, hyper-parameter tuning;

  • Applications: advertisements, autonomous driving, business, chemical synthesis, conversational AI, drawing, drug design, education, energy, finance, healthcare, industrial control, music, recommender systems, robotics, transportation, or other problems in science, engineering and arts.


Paper Submission

Deadline: June 15, 2020

Notification: June 21, 2020

Submission website

Style files. (Changed the header to "Presented as a poster at RL4RealLife 2020".) We will recommend submissions to use these provided style files (adapted from ICLR files). For previously published papers, feel free to use previous formats.

Accepted papers are non-archival and non-peer-reviewed. We welcome submissions of recently published work.

Authors will pre-record video presentation of their work, and they will also host their own video conferencing channels (with Zoom/Google Hangout) during the online poster session portion of the conference.

We do not set the length of a pre-recorded talk for your flexibility. We recommend 5 minutes for a concise introduction, or up to 20 minutes for a full discussion, but not exceeding 30 minutes.


List of papers

Poster info spreadsheet

Deterministic Policy Gradient with Multi-Objective Rewards, Xu Chen, Yali Du, Jun Wang, paper, video

Multi Type Mean Field Reinforcement Learning, Sriram Ganapathi Subramanian, Pascal Poupart, Matthew E. Taylor, and Nidhi Hegde, paper, video

Reinforcement Learning for Personalization: A Systematic Literature Review, Floris den Hengst, Eoin Martino Grua, Ali el Hassouni, and Mark Hoogendoorn, paper, video

Sample Efficient Policy Gradient Methods with Recursive Variance Reduction, Pan Xu, Felicia Gao, Quanquan Gu, paper, video

Automated Optical Multi-Layer Design via Deep Reinforcement Learning, Haozhu Wang, Zeyu Zheng, Chengang Ji, L. Jay Guo, paper, video

BADGR: An Autonomous Self-Supervised Learning-Based Navigation System, Gregory Kahn, Pieter Abbeel, Sergey Levine , paper, video

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation, Aaron Sonabend, Junwei Lu, Leo A. Celi, Tianxi Cai, Peter Szolovits, paper, video

Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads, Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, Sergey Levine , paper, video

Neural Dynamic Policies for End-to-End Sensorimotor Learning, Shikhar Bahl, Mustafa Mukadam, Abhinav Gupta, Deepak Pathak, paper, video

Neural Approximate Dynamic Programming for On-Demand Ride-Pooling, Sanket Shah, Meghna Lowalekar, Pradeep Varakantham, paper, video

Advancing Renewable Electricity Consumption With Reinforcement Learning, Filip Tolovski, paper, video

D4RL: Datasets for Deep Data-Driven Reinforcement Learning, Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine, paper, video

AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos, Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine, paper, video

Value Variance Minimization for Learning Approximate Equilibrium in Stochastic Congestion Games, Tanvi Verma, Pradeep Varakantham, paper, video

Incentive Based Learning for Improving Effectiveness of Aggregation Systems, Tanvi Verma, Pradeep Varakantham, paper, video

AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning, Lu Chen, Zhi Chen, Bowen Tan, Sishan Long, Milica Gasic, Kai Yu, paper, video

Planning and Execution using Inaccurate Models with Provable Guarantees, Anirudh Vemula, Yash Oza, J. Andrew Bagnell, Maxim Likhachev, paper, video

Enabling Safe Exploration of Action Space in Real--World Robots, Shivam Garg, Homayoon Farrahi, A.Rupam Mahmood, paper, video

Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning, Zhenyu Shou, Xuan Di, paper, video

Sparsity-Agnostic Lasso Bandit, Min-hwan Oh, Garud Iyengar, Assaf Zeevi, paper, video

Variance Reduction for Evolutionary Strategies via Structured Control Variate, Yunhao Tang, Krzysztof Choromanski, Alp Kucukelbir, paper

Reinforcement Learning for Integer Programming: Learning to Cut, Yunhao Tang, Shipra Agrawal, Yuri Faenza, paper, video

Delay-Correcting Reinforcement Learning, Yann Bouteiller*, Simon Ramstedt*, Giovanni Beltrame, Christopher Pal, Jonathan Binas, paper, video

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors, Karl Pertsch*, Oleh Rybkin*, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine, paper, video

Treatment Effect Information Transfer from Well Curated to Less Resourced Populations with HIV, Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-Velez, paper, video

Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion, Josh Roy, George Konidaris, paper, video

Learning to Dock Robustly, Mohamed Elsayed, Hager Radi, A. Rupam Mahmood, video

Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation, Ryan Julian, Benjamin Swanson, Gaurav Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman, paper, video

An Empirical Investigation of the Challenges of Real World Reinforcement Learning, Gabriel Dulac-Arnold, Nir Levine, Daniel J. Mankowitz, Cosmin Paduraru, Sven Gowal, Jerry Li, Todd Hester, paper, video

Student-teacher curriculum learning via reinforcement learning: Inpatient Hospital Admission Prediction, Rasheed el-Bouri, David Eyre, Peter Watkinson, Tingting Zhu, David Clifton, paper, video

Conservative Q-learning for Offline Reinforcement Learning, Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine, paper

Latent State Models for Meta-Reinforcement Learning, Anusha Nagabandi*, Tony Zhao*, Kate Rakelly*, Chelsea Finn, Sergey Levine, paper, video

RL Unplugged: Benchmarks for Offline Reinforcement Learning, Caglar Gulcehre*, Ziyu Wang*, Alexander Novikov*, Tom Le Paine*, Sergio Gómez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess and Nando de Freitas, paper, video

Intrinsic Reward Driven Imitation Learning via Generative Model, Xingrui Yu, Yueming Lyu and Ivor W. Tsang, paper, video

Learning to Run a Power Network: bringing AI to power grid control, Benjamin Donnot, Antoine Marot, Isabelle Guyon, paper, video

Automation of the Fresh Food Supply Chain Using Model-Based Planning, Harlan Seymour, Andy Chen, Philip Cerles, Sawyer Birnbaum, Siddarth Sampangi, Danny Nemer, Volodymyr Kuleshov, paper

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions, Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez, paper, video

Planning to Explore via Self-Supervised World Models, Ramanan Sekar*, Oleh Rybkin*, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak, paper, video

Communication

Real-time Text-based Chat

Authors of posters can create their own topics. Audience can have discussions before/during/after the virtual conference. Organizers can make announcements. You are welcome to join our Slack Workspace for RL for Real Life.

Virtual Booths

We encourage participants to host virtual booths to discuss research topics, to look for job opportunities, to social, etc.

Twitter

#RL4RealLife

Contact by email

yuxili@gmail.com

Co-Chairs

Gabriel Dulac-Arnold (Google Research)

Alborz Geramifard (Facebook AI)

Omer Gottesman (Harvard)

Lihong Li (Google Research)

Yuxi Li (Attain.ai)

Anusha Nagabandi (UC Berkeley & Google)

Csaba Szepesvari (Deepmind & U. of Alberta)