2019
AAAI
AAAI 2019
Learning Options with Interest Functions
Abstract
Abstract Learning temporal abstractions which are partial solutions to a task and could be reused for solving other tasks is an ingredient that can help agents to plan and learn efficiently. In this work, we tackle this problem in the options framework. We aim to autonomously learn options which are specialized in different state space regions by proposing a notion of interest functions, which generalizes initiation sets from the options framework for function approximation. We build on the option-critic framework to derive policy gradient theorems for interest functions, leading to a new interest-option-critic architecture.
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Conference Pioneer
— AAAI 2019
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning and Reinforcement Learning
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Keyword Pioneer
— interest function
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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, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Artificial Intelligence > Core AI > Planning
Reinforcement Learning > Methods > Policy Learning
Machine Learning > Learning Paradigms > Meta-Learning
Machine Learning > Learning Types > Meta-Learning
Deep Learning > Learning Types > Reinforcement Learning
Artificial Intelligence > Core AI > Reinforcement Learning