530 research outputs found
Influence-Optimistic Local Values for Multiagent Planning --- Extended Version
Recent years have seen the development of methods for multiagent planning
under uncertainty that scale to tens or even hundreds of agents. However, most
of these methods either make restrictive assumptions on the problem domain, or
provide approximate solutions without any guarantees on quality. Methods in the
former category typically build on heuristic search using upper bounds on the
value function. Unfortunately, no techniques exist to compute such upper bounds
for problems with non-factored value functions. To allow for meaningful
benchmarking through measurable quality guarantees on a very general class of
problems, this paper introduces a family of influence-optimistic upper bounds
for factored decentralized partially observable Markov decision processes
(Dec-POMDPs) that do not have factored value functions. Intuitively, we derive
bounds on very large multiagent planning problems by subdividing them in
sub-problems, and at each of these sub-problems making optimistic assumptions
with respect to the influence that will be exerted by the rest of the system.
We numerically compare the different upper bounds and demonstrate how we can
achieve a non-trivial guarantee that a heuristic solution for problems with
hundreds of agents is close to optimal. Furthermore, we provide evidence that
the upper bounds may improve the effectiveness of heuristic influence search,
and discuss further potential applications to multiagent planning.Comment: Long version of IJCAI 2015 paper (and extended abstract at AAMAS
2015
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
Energy Efficient Execution of POMDP Policies
Recent advances in planning techniques for partially observable Markov decision processes have focused on online search techniques and offline point-based value iteration. While these techniques allow practitioners to obtain policies for fairly large problems, they assume that a non-negligible amount of computation can be done between each decision point. In contrast, the recent proliferation of mobile and embedded devices has lead to a surge of applications that could benefit from state of the art planning techniques if they can operate under severe constraints on computational resources. To that effect, we describe two techniques to compile policies into controllers that can be executed by a mere table lookup at each decision point. The first approach compiles policies induced by a set of alpha vectors (such as those obtained by point-based techniques) into approximately equivalent controllers, while the second approach performs a simulation to compile arbitrary policies into approximately equivalent controllers. We also describe an approach to compress controllers by removing redundant and dominated nodes, often yielding smaller and yet better controllers. Further compression and higher value can sometimes be obtained by considering stochastic controllers. The compilation and compression techniques are demonstrated on benchmark problems as well as a mobile application to help persons with Alzheimer's to way-find. The battery consumption of several POMDP policies is compared against finite-state controllers learned using methods introduced in this paper. Experiments performed on the Nexus 4 phone show that finite-state controllers are the least battery consuming POMDP policies
Nonapproximability Results for Partially Observable Markov Decision Processes
We show that for several variations of partially observable Markov decision
processes, polynomial-time algorithms for finding control policies are unlikely
to or simply don't have guarantees of finding policies within a constant factor
or a constant summand of optimal. Here "unlikely" means "unless some complexity
classes collapse," where the collapses considered are P=NP, P=PSPACE, or P=EXP.
Until or unless these collapses are shown to hold, any control-policy designer
must choose between such performance guarantees and efficient computation
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