Qlass: an Enhancement of Q-learning to Generate State Space Adaptively
نویسندگان
چکیده
In this paper, we propose Q-learning with adaptive state segmentation (QLASS). QLASS provides an e cient method to construct state space suitable for Q-learning to accomplish the task in a continuous sensor space. In QLASS, the robot starts with single state covering whole sensor space. The sensor space is segmented incrementally based on sensor vectors and reinforcement signals. The segmented sub-space of sensor space is registered as a new state. Experimental results show that QLASS can segment the sensor space e ectively to accomplish the task. Further, acquired state space reveals the tness landscape in a Voronoi tessellation.
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