ICML 2019
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Real-world Sequential Decision Making: Reinforcement Learning and Beyond

Hoang Le · Yisong Yue · Adith Swaminathan · Byron Boots · Ching-An Cheng

Seaside Ballroom

Workshop website: https://realworld-sdm.github.io/

This workshop aims to bring together researchers from industry and academia in order to describe recent advances and discuss future research directions pertaining to real-world sequential decision making, broadly construed. We aim to highlight new and emerging research opportunities for the machine learning community that arise from the evolving needs for making decision making theoretically and practically relevant for realistic applications.

Research interest in reinforcement and imitation learning has surged significantly over the past several years, with the empirical successes of self-playing in games and availability of increasingly realistic simulation environments. We believe the time is ripe for the research community to push beyond simulated domains and start exploring research directions that directly address the real-world need for optimal decision making. We are particularly interested in understanding the current theoretical and practical challenges that prevent broader adoption of current policy learning and evaluation algorithms in high-impact applications, across a broad range of domains.

This workshop welcomes both theory and application contributions.

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Timezone: America/Los_Angeles


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