Reinforcement Learning
Approximation spaces in off-policy Monte Carlo learning
Engineering Applications of Artificial Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Heuristic search based exploration in reinforcement learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A new Q-learning algorithm based on the metropolis criterion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cooperative strategy based on adaptive Q-learning for robot soccer systems
IEEE Transactions on Fuzzy Systems
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For measuring the uncertainty of behavior, the average rough coverage doesn't consider the difference among middle learning stages in reinforcement learning. To address this problem, a novel measure model based on generalized approximation spaces is proposed. In this study, uncertainty is regarded as the local feature of a state and used to guide future learning. Data-driven Qlearning based this novel model is presented for improvement of strategies based exploration. The measure function of uncertainty is used to control the balance between exploration and exploitation. Experiment results show that data-driven reinforcement learning is effective.