2019
AAAI
AAAI 2019
QUOTA: The Quantile Option Architecture for Reinforcement Learning
Abstract
Abstract In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL). In QUOTA, decision making is based on quantiles of a value distribution, not only the mean. QUOTA provides a new dimension for exploration via making use of both optimism and pessimism of a value distribution. We demonstrate the performance advantage of QUOTA in both challenging video games and physical robot simulators.
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Conference Pioneer
— AAAI 2019
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Topic Pioneer
— Distributional Reinforcement Learning
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Reinforcement Learning
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Keyword Pioneer
— quantile-based exploration
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
Authors
Topics
Artificial Intelligence > Core AI > Game AI
Reinforcement Learning > Methods > Deep RL
Reinforcement Learning > Methods > Policy Learning
Reinforcement Learning > Applications > Game AI
Machine Learning > Learning Types > Reinforcement Learning
Deep Learning > Learning Types > Reinforcement Learning
Machine Learning > Learning Types > Exploration
Machine Learning > Learning Types > Distributional Reinforcement Learning