Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Temporal sampling annealing schemes for receding
horizon multi-agent planning
A. Ma, M. Ouimet, J. Cortés
Robotics and Autonomous Systems 143 (2021), 103823
Abstract
This paper deals with multi-agent scenarios where
individual agents must coordinate their plans in
order to efficiently complete a set of tasks. Our
strategy formulates the task planning problem as a
potential game and uses distributed stochastic
sampling policies to reach a consensus on which
sequences of actions agents should take. We execute
this over a receding finite time horizon and take
special care to discourage agents from breaking
promises in the near future, which may cause other
agents to unsuccessfuly attempt a joint action. At
the same time, we allow agents to change plans in
the distant future, as this gives time for other
agents to adapt their plans, allowing the team to
escape locally optimal solutions. To do this we
introduce two sampling schemes for new actions: a
geometric-based scheme, where the probability of
sampling a new action increases geometrically in
time, and an inference-based sampling scheme, where
a convolutional neural network provides
recommendations for joint actions. We test the
proposed schemes in a cooperative orienteering
environment to illustrate their performance and
validate the intuition behind their design.
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Mechanical and Aerospace Engineering,
University of California, San Diego
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