19,709 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
Bayesian learning of noisy Markov decision processes
We consider the inverse reinforcement learning problem, that is, the problem
of learning from, and then predicting or mimicking a controller based on
state/action data. We propose a statistical model for such data, derived from
the structure of a Markov decision process. Adopting a Bayesian approach to
inference, we show how latent variables of the model can be estimated, and how
predictions about actions can be made, in a unified framework. A new Markov
chain Monte Carlo (MCMC) sampler is devised for simulation from the posterior
distribution. This step includes a parameter expansion step, which is shown to
be essential for good convergence properties of the MCMC sampler. As an
illustration, the method is applied to learning a human controller
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