Optimally Solving Dec-POMDPs as Continuous-State MDPs

@article{Dibangoye2016OptimallySD,
  title={Optimally Solving Dec-POMDPs as Continuous-State MDPs},
  author={Jilles Steeve Dibangoye and Christopher Amato and Olivier Buffet and François Charpillet},
  journal={J. Artif. Intell. Res.},
  year={2016},
  volume={55},
  pages={443-497}
}
Optimally solving decentralized partially observable Markov decision processes (Dec-POMDPs) is a hard combinatorial problem. Current algorithms search through the space of full histories for each agent. Because of the doubly exponential growth in the number of policies in this space as the planning horizon increases, these methods quickly become intractable. However, in real world problems, computing policies over the full history space is often unnecessary. True histories experienced by the… CONTINUE READING