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Reinforcement Learning with People
Emma Brunskill

Mon Dec 04 08:00 AM -- 10:15 AM (PST) @ Hall C

There has been recent very exciting advances in (deep) reinforcement learning, particularly in the areas of games and robotics. Yet perhaps the largest impact could come when reinforcement learning systems interact with people. In this tutorial we will discuss work on reinforcement learning for helping and assisting people, and frameworks and approaches for enabling people helping reinforcement learning. We will cover

Background on reinforcement learning. Reinforcement learning for people-focused applications Approaches for enabling people to assist reinforcement learners

A number of the ideas presented here will also be relevant to many high stakes reinforcement learning systems.

Target audience: The majority of the tutorial will be aimed at an audience who has a basic machine learning background (e.g. as acquired by a class or equivalent).

Learning objectives: Know some of the key technical challenges that arise for reinforcement learning in people-focusing domains; understand some of the algorithms and approaches that have been developed to address these challenges; become familiar with some of the other application areas that have also or can also benefit from reinforcement learning.

Author Information

Emma Brunskill (Stanford University)

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