5,094 research outputs found
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
Consensus as a Nash Equilibrium of a Dynamic Game
Consensus formation in a social network is modeled by a dynamic game of a
prescribed duration played by members of the network. Each member independently
minimizes a cost function that represents his/her motive. An integral cost
function penalizes a member's differences of opinion from the others as well as
from his/her own initial opinion, weighted by influence and stubbornness
parameters. Each member uses its rate of change of opinion as a control input.
This defines a dynamic non-cooperative game that turns out to have a unique
Nash equilibrium. Analytic explicit expressions are derived for the opinion
trajectory of each member for two representative cases obtained by suitable
assumptions on the graph topology of the network. These trajectories are then
examined under different assumptions on the relative sizes of the influence and
stubbornness parameters that appear in the cost functions.Comment: 7 pages, 9 figure, Pre-print from the Proceedings of the 12th
International Conference on Signal Image Technology and Internet-based
Systems (SITIS), 201
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