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Reinforcement Learning for Real Life

Yuxi Li · Minmin Chen · Omer Gottesman · Lihong Li · Zongqing Lu · Rupam Mahmood · Niranjani Prasad · Zhiwei (Tony) Qin · Csaba Szepesvari · Matthew Taylor

Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm and applies broadly in many disciplines, including science, engineering and humanities. RL has seen prominent successes in many problems, such as games, robotics, recommender systems. 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): 1) practical RL algorithms, which covers all algorithmic challenges of RL, especially those that directly address challenges faced by real-world applications; 2) practical issues: generalization, sample efficiency, exploration, reward, scalability, model-based learning, prior knowledge, safety, accountability, interpretability, reproducibility, hyper-parameter tuning; and 3) applications.

We have 6 premier panel discussions and 70+ great papers/posters. Welcome!

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Timezone: America/Los_Angeles