Technical Note: \cal Q-Learning
Machine Learning
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Adaptive Behavior - Special issue on biologically inspired models of navigation
Self-Organizing Maps
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Motion sketch: acquisition of visual motion guided behaviors
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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In a robot system, it is important to consider how the outside environment is expressed as a state using sensor information. In this study, we provide a state representation that can express the sensor output changed by environmental change as the same state. It assumes that sensor outputs are probability distributions, and the distances between the distributions of each sensor's output are used to express a state. To confirm the effectiveness of the proposed state representation, we conducted experiments using a mobile robot. The results confirmed that the proposed representation could recognize similar states using a converted sensor output.