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How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the parameter by the incremental training set stage...
Reinforcement learning method usually require that all actions be tried in all state infinitely often for convergence. Such algorithms are impractical to be applied to sophisticated systems due to its low learning efficiency. This paper analyses the problem of limit cycles exist in reinforcement learning for inverted pendulum system control and proposed active exploration planning policy. The algorithm...
Model selection is one of the central problems of machine learning. The goal of model selection is to select from a set of competing explanations the best one that capture the underlying regularities of given observations. The criterion of a good model is generalizability. We must make balance between the goodness of fit and the complexity of the model to obtain good generalization. Most of present...
To accelerate the learning of reinforcement learning, many types of function approximation are used to represent state value. However function approximation reduces the accuracy of state value, and brings difficulty in the convergence. To solve the problems of tradeoff between the generalization and accuracy in reinforcement learning, we represent state-action value by two CMAC networks with different...
Q-learning is one of the successfully established algorithms for the reinforcement learning, which has been widely used to the intelligent control system, such as the control of robot pose. However, curse of dimensionality and difficulty in convergence exist in Q-learning arising from random exploration policy. In this paper, we propose a greedy exploration policy of Q-learning with rule guidance...
Control inverted pendulum is one of important applied regions of reinforcement learning. This paper analyzes negative effect on the control of inverted pendulum caused by the limit cycle. It points out the limit cycle will make Q-value converge to zero, and destroy the stabilization of the optimal control policy. Moreover higher degree of exploration can not overcome this problem, but rather intensify...
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