Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
Technical Note: \cal Q-Learning
Machine Learning
Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Efficient SVM Regression Training with SMO
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
A reinforcement learning neural network for adaptive control of Markov chains
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We consider the problem of approximating the cost-to-go functions in reinforcement learning. By mapping the state implicitly into a feature space, we perform a simple algorithm in the feature space, which corresponds to a complex algorithm in the original state space. Two kernel-based reinforcement learning algorithms, the ε -insensitive kernel based reinforcement learning (ε – KRL) and the least squares kernel based reinforcement learning (LS-KRL) are proposed. An example shows that the proposed methods can deal effectively with the reinforcement learning problem without having to explore many states.