2014
NIPS
NeurIPS 2014
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning
Abstract
We describe how to use robust Markov decision processes for value function approximation with state aggregation. The robustness serves to reduce the sensitivity to the approximation error of sub-optimal policies in comparison to classical methods such as fitted value iteration. This results in reducing the bounds on the gamma-discounted infinite horizon performance loss by a factor of 1/(1-gamma) while preserving polynomial-time computational complexity. Our experimental results show that using the robust representation can significantly improve the solution quality with minimal additional computational cost.
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Interdisciplinary Bridge
— Machine Learning and Reinforcement Learning
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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
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Trend Setter
— Robustness
Authors
Topics
Machine Learning > Optimization & Theory > Optimization
Machine Learning > Application Areas > Domain Adaptation
Reinforcement Learning > Methods > Deep RL
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Learning Types > Deep Learning
Machine Learning > Core Methods > Optimization
Machine Learning > Learning Types > Robustness