Conference Paper

Multilayered reinforcement learning for complicated collision avoidance problems

RIKEN, Inst. of Phys. & Chem. Res., Saitama
DOI: 10.1109/ROBOT.1998.680648 Conference: Robotics and Automation, 1998. Proceedings. 1998 IEEE International Conference on, Volume: 3
Source: IEEE Xplore

ABSTRACT We have proposed the collision avoidance methods in a multirobot
system based on the information exchanged by the “LOCISS: Locally
Communicable Infrared Sensory System”, which is developed by the
authors. One of the problems in the LOCISS based methods is that the
number of situations which should be considered increases very much when
the number of the robots and stationary obstacles in the working
environment increases. In order to reduce the required computational
power and memory capacity for such a large number of situations, we
propose, in this paper, a multilayered reinforcement learning scheme to
acquire appropriate collision avoidance behaviors. The feasibility and
the performance of the proposed scheme is examined through the
experiment using actual mobile robots

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