2013
NIPS
NeurIPS 2013
Reinforcement Learning in Robust Markov Decision Processes
Abstract
An important challenge in Markov decision processes is to ensure robustness with respect to unexpected or adversarial system behavior while taking advantage of well-behaving parts of the system. We consider a problem setting where some unknown parts of the state space can have arbitrary transitions while other parts are purely stochastic. We devise an algorithm that is adaptive to potentially adversarial behavior and show that it achieves similar regret bounds as the purely stochastic case.
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Interdisciplinary Bridge
— Machine Learning and Reinforcement Learning
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Keyword Pioneer
— robust markov decision
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Hot Topic Early Bird
— adversarial robustness
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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Trend Setter
— Robustness
Authors
Topics
Artificial Intelligence > Core AI > Agent Systems
Machine Learning > Learning Types > Adversarial Learning
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Optimization
Reinforcement Learning > Methods > Deep RL
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Optimization & Theory > Online Algorithms
Machine Learning > Learning Types > Robustness