7,281 research outputs found
Bayesian Quadratic Network Game Filters
A repeated network game where agents have quadratic utilities that depend on
information externalities -- an unknown underlying state -- as well as payoff
externalities -- the actions of all other agents in the network -- is
considered. Agents play Bayesian Nash Equilibrium strategies with respect to
their beliefs on the state of the world and the actions of all other nodes in
the network. These beliefs are refined over subsequent stages based on the
observed actions of neighboring peers. This paper introduces the Quadratic
Network Game (QNG) filter that agents can run locally to update their beliefs,
select corresponding optimal actions, and eventually learn a sufficient
statistic of the network's state. The QNG filter is demonstrated on a Cournot
market competition game and a coordination game to implement navigation of an
autonomous team
Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks
The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented
for the problem of tracking a target dynamic model using a time-varying network
of heterogeneous sensing agents. In the DBF algorithm, the sensing agents
combine their normalized likelihood functions in a distributed manner using the
logarithmic opinion pool and the dynamic average consensus algorithm. We show
that each agent's estimated likelihood function globally exponentially
converges to an error ball centered on the joint likelihood function of the
centralized multi-sensor Bayesian filtering algorithm. We rigorously
characterize the convergence, stability, and robustness properties of the DBF
algorithm. Moreover, we provide an explicit bound on the time step size of the
DBF algorithm that depends on the time-scale of the target dynamics, the
desired convergence error bound, and the modeling and communication error
bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into
a modified form of the Kalman information filter. The performance and robust
properties of the DBF algorithm are validated using numerical simulations
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
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