2013
ICML
ICML 2013
Temporal Difference Methods for the Variance of the Reward To Go
Abstract
In this paper we extend temporal difference policy evaluation algorithms to performance criteria that include the variance of the cumulative reward. Such criteria are useful for risk management, and are important in domains such as finance and process control. We propose variants of both TD(0) and LSTD(λ) with linear function approximation, prove their convergence, and demonstrate their utility in a 4-dimensional continuous state space problem.
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Conference Pioneer
— ICML 2013
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization and Reinforcement Learning
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Trend Setter
— Risk Management
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Hot Topic Early Bird
— reinforcement learning
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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