  The Common Information (CI) approach provides a systematic way to transform a
multi-agent stochastic control problem to a single-agent partially observed
Markov decision problem (POMDP) called the coordinator's POMDP. However, such a
POMDP can be hard to solve due to its extraordinarily large action space. We
propose a new algorithm for multi-agent stochastic control problems, called
coordinator's heuristic search value iteration (CHSVI), that combines the CI
approach and point-based POMDP algorithms for large action spaces. We
demonstrate the algorithm through optimally solving several benchmark problems.
