2018
IJCAI
IJCAI 2018
Improving Reinforcement Learning with Human Input
Abstract
Reinforcement learning (RL) has had many successes when learning autonomously. This paper and accompanying talk consider how to make use of a non-technical human participant, when available. In particular, we consider the case where a human could 1) provide demonstrations of good behavior, 2) provide online evaluative feedback, or 3) define a curriculum of tasks for the agent to learn on. In all cases, our work has shown such information can be effectively leveraged. After giving a high-level overview of this work, we will highlight a set of open questions and suggest where future work could be usefully focused.
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Interdisciplinary Bridge
— Artificial Intelligence and Reinforcement Learning
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Trend Setter
— Human-AI Interaction
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Hot Topic Early Bird
— curriculum learning
Authors
Topics
Artificial Intelligence > Core AI > Human-AI Interaction
Reinforcement Learning > Methods > Deep RL
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Learning Types > Imitation Learning
Deep Learning > Learning Types > Imitation Learning
Machine Learning > Learning Paradigms > Imitation Learning