An Behavior-based Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Biologically inspired robot behavior engineering
Biologically inspired robot behavior engineering
Regional Cooperative Multi-agent Q-learning Based on Potential Field
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 06
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This article is concerned with autonomous planninng of diverse cooperative robot actions. In this work complex cooperative actions are realized based upon the Intelligent Composite Motion Control, which is a learning methodology for intelligent robots that gradually realize complex actions from fundamental motions. For efficient construction of action intelligence Multi-stage Genetic Algorithm, MGA, is used. The MGA solves a large scale optimization problem with complicated constraints as multi-stage but small scale combinatorial optimization problems with simple constraints, which are solved by GA to generate their suboptimal solution sets. In order to realize autonomous planning of diverse cooperation according to situation, Variable-chromosome-length Genetic Algorithm (VGA) is introduced and combined to MGA. The presented method is successfully applied to planning of diverse cooperative robot soccer actions according to situation.