  To deal with the prohibitive complexity of calculating policies in
Decentralized MDPs, researchers have proposed models that exploit structured
agent interactions. Settings where most agent actions are independent except
for few actions that affect the transitions and/or rewards of other agents can
be modeled using Event-Driven Interactions with Complex Rewards (EDI-CR).
Finding the optimal joint policy can be formulated as an optimization problem.
However, existing formulations are too verbose and/or lack optimality
guarantees. We propose a compact Mixed Integer Linear Program formulation of
EDI-CR instances. The key insight is that most action sequences of a group of
agents have the same effect on a given agent. This allows us to treat these
sequences similarly and use fewer variables. Experiments show that our
formulation is more compact and leads to faster solution times and better
solutions than existing formulations.
