2013
NeurIPS
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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Topic Pioneer
- Robustness
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Interdisciplinary Bridge
- Artificial Intelligence and Reinforcement Learning
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Trend Setter
- Markov Decision Process
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Keyword Pioneer
- robust reinforcement learning
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Hot Topic Early Bird
- markov decision process
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
Artificial Intelligence > Core AI > Reinforcement Learning
Machine Learning > Learning Types > Robustness
Artificial Intelligence > Core AI > Robustness
Machine Learning > Learning Types > Markov Decision Process