4,715 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
New Integrality Gap Results for the Firefighters Problem on Trees
The firefighter problem is NP-hard and admits a approximation based
on rounding the canonical LP. In this paper, we first show a matching
integrality gap of on the canonical LP. This result relies
on a powerful combinatorial gadget that can be used to prove integrality gap
results for many problem settings. We also consider the canonical LP augmented
with simple additional constraints (as suggested by Hartke). We provide several
evidences that these constraints improve the integrality gap of the canonical
LP: (i) Extreme points of the new LP are integral for some known tractable
instances and (ii) A natural family of instances that are bad for the canonical
LP admits an improved approximation algorithm via the new LP. We conclude by
presenting a integrality gap instance for the new LP.Comment: 22 page
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