2013
NIPS
NeurIPS 2013
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces
Abstract
This paper addresses the problem of automatic generation of features for value function approximation in reinforcement learning. Bellman Error Basis Functions (BEBFs) have been shown to improve the error of policy evaluation with function approximation, with a convergence rate similar to that of value iteration. We propose a simple, fast and robust algorithm based on random projections, which generates BEBFs for sparse feature spaces. We provide a finite sample analysis of the proposed method, and prove that projections logarithmic in the dimension of the original space guarantee a contraction in the error. Empirical results demonstrate the strength of this method in domains in which choosing a good state representation is challenging.
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Interdisciplinary Bridge
— Machine Learning and Reinforcement Learning
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Keyword Pioneer
— feature generation
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
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Trend Setter
— Value Iteration
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Hot Topic Early Bird
— reinforcement learning
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Optimization
Reinforcement Learning > Methods > Deep RL
Reinforcement Learning > Applications > Robotics
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Core Methods > Feature Learning
Reinforcement Learning > Methods > Value Iteration
Deep Learning > Learning Types > Reinforcement Learning