Developing scalable algorithms for solving partially observable Markov decision processes (POMDPs) is an important challenge. One promising approach is based on representing POMDP...
Christopher Amato, Daniel S. Bernstein, Shlomo Zil...
Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents...
We present a new algorithm, called incremental least squares policy iteration (ILSPI), for finding the infinite-horizon stationary policy for partially observable Markov decision ...
Decentralized partially observable Markov decision processes (Dec-POMDPs) constitute an expressive framework for multiagent planning under uncertainty, but solving them is provabl...
Frans A. Oliehoek, Matthijs T. J. Spaan, Shimon Wh...
We consider the problem of estimating the policy gradient in Partially Observable Markov Decision Processes (POMDPs) with a special class of policies that are based on Predictive ...