An Behavior-based Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Lookahead and pathology in decision tree induction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reinforcement learning-based output feedback control of nonlinear systems with input constraints
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Look-ahead based fuzzy decision tree induction
IEEE Transactions on Fuzzy Systems
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To design appropriate actions of mobile robots, the designers usually observe the sensory signals on the robots and decide the actions from the viewpoint of some desired purposes. This approach needs deliberative consideration and abundant knowledge on robotics for a variety of situations. To improve the actions of robots, it is hard to sense the error by human eyes and takes time in trial-and-error. In this article, we propose a novel learning algorithm, Fused Behavior Q-Learning algorithm (FBQL) to deal with such situations. The proposed algorithm has the merit of simplicity in designing individual behavior by means of a decision tree approach to state aggregation which is eventually recoding the domain knowledge. Furthermore, these learned behaviors are fused into a more complicated behavior by a set of appropriate weighting parameters through a Q-learning mechanism such that the robots can behave adaptively and optimally in a dynamic environment.