Title:
Learning to Role-Switch in Multi-Robot Systems
Learning to Role-Switch in Multi-Robot Systems
Author(s)
Arkin, Ronald C.
Martinson, Eric
Martinson, Eric
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Abstract
We present an approach that uses Q-learning on
individual robotic agents, for coordinating a mission-tasked
team of robots in a complex scenario. To reduce
the size of the state space, actions are grouped into sets of
related behaviors called roles and represented as
behavioral assemblages. A role is a Finite State Automata
such as Forager, where the behaviors and their
sequencing for finding objects, collecting them, and
returning them are already encoded and do not have to be
relearned. Each robot starts out with the same set of
possible roles to play, the same perceptual hardware for
coordination, and no contact other than perception
regarding other members of the team. Over the course of
training, a team of Q-learning robots will converge to
solutions that best the performance of a well-designed
handcrafted homogeneous team.
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Date Issued
2003
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Text
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Paper