Call for virtual poster session
Reinforcement learning as a field that studies the problem of sequential decision making with unknown and potentially long-term consequences. Reinforcement learning is a multi-disciplinary topic, bringing together diverse fields of study including computer science, cognitive science, mathematics, psychology, economics, control theory, and neuroscience. The common theme that connects these fields, and the core goal of reinforcement learning is the question: How do natural and artificial systems learn to make decisions in complex, unknown environments based on limited, noisy, and possibly delayed feedback?
This virtual workshop aims to bring together researchers from industry and academia to share and discuss recent advances, challenges, and future research directions for reinforcement learning. Our goal is to highlight emerging research opportunities for the reinforcement learning community, particularly those driven by the evolving need for robust decision making in practical applications. Reinforcement Learning Day 2021 will provide an opportunity for different research communities to learn from each other and build on the latest knowledge in reinforcement learning and related disciplines.
Reinforcement Learning Day 2021 will feature invited talks and conversations with leaders in the field, including Yoshua Bengio and John Langford, whose research covers a broad array of topics related to reinforcement learning. For more details please see the agenda page.
Virtual poster session
In addition to our speaker program, Reinforcement Learning Day 2021 will include a virtual poster session, showcasing recent and ongoing research in all areas of reinforcement learning.
We invite you to submit posters on all topics related to reinforcement learning. Suggested topics include (but are certainly not limited to):
- Deep Reinforcement Learning
- Reinforcement Learning Theory
- Bandit Algorithms
- Multi-Agent Reinforcement Learning
- Reinforcement Learning Benchmarks and Datasets
- Reinforcement Learning with Natural Language
- Human-in-the-Loop Reinforcement Learning
- Imitation Learning
- Control Theory
- Cross-Disciplinary Research with Reinforcement Learning: Structured Prediction, Game Theory, Operation Research, Fairness, Active Learning, Causality, Privacy, etc.
- Applications of Reinforcement Learning: Recommender Systems, Robotics, Healthcare, Education, Conversational AI, Gaming, Finance, Neuroscience, Manufacturing etc.
What to submit
We invite the submission of extended abstracts (1-4 pages) on topics related to reinforcement learning. Authors of accepted abstracts will be invited to present their work at our virtual poster session (via Microsoft Teams), giving authors the opportunity for in-depth discussions with other Reinforcement Learning Day 2021 participants, presenters, and Microsoft researchers. Abstract reviewing will be single-blind. From the applications, we will be accepting 10-15 presenters only. Accepted presenters will be asked to prepare pre-recorded video presentations to complement the live discussion during the virtual poster session.
Please submit your abstract to email@example.com.
- December 11, 2020: Abstract submission deadline
- December 22, 2020: Author notification
- January 14, 2021: Reinforcement Learning Day 2021 – virtual workshop!