Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel-Based Reinforcement Learning
Machine Learning
Sparse Online Greedy Support Vector Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Reinforcement learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The projectron: a bounded kernel-based Perceptron
Proceedings of the 25th international conference on Machine learning
Kernelized value function approximation for reinforcement learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Gaussian processes for sample efficient reinforcement learning with RMAX-like exploration
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
A sparse kernel-based least-squares temporal difference algorithm for reinforcement learning
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Kernel-Based Least Squares Policy Iteration for Reinforcement Learning
IEEE Transactions on Neural Networks
Gradient based algorithms with loss functions and kernels for improved on-policy control
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
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We introduce the first online kernelized version of SARSA(λ) to permit sparsification for arbitrary λ for 0 ≤ λ ≤ 1; this is possible via a novel kernelization of the eligibility trace that is maintained separately from the kernelized value function. This separation is crucial for preserving the functional structure of the eligibility trace when using sparse kernel projection techniques that are essential for memory efficiency and capacity control. The result is a simple and practical Kernel-SARSA(λ) algorithm for general 0 ≤ λ ≤ 1 that is memory-efficient in comparison to standard SARSA(λ) (using various basis functions) on a range of domains including a real robotics task running on a Willow Garage PR2 robot.