Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics
This work addresses scalability issues in multi-agent planning for applications like multi-robot navigation, but it appears incremental as it builds on existing frameworks.
The paper tackles the problem of solving large-scale deterministic decentralized POMDPs (Det-Dec-POMDPs) for multi-agent planning, introducing a practical solver called IDPP that is optimized to handle domains where current solvers are inefficient, though no concrete performance numbers are provided.
Many high-level multi-agent planning problems, including multi-robot navigation and path planning, can be effectively modeled using deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs). This is a subclass of Dec-POMDPs characterized by deterministic transitions and observations conditioned on the state and joint actions. We then propose a practical solver called Iterative Deterministic POMDP Planning (IDPP). This method builds on the classic Joint Equilibrium Search for Policies framework and is specifically optimized to handle large-scale Det-Dec-POMDPs that current Dec-POMDP solvers are unable to address efficiently.