# Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics

## TL;DR

This paper introduces Deterministic Dec-POMDPs, a subclass of multi-agent planning problems with deterministic dynamics, and proposes a scalable solver called IDPP optimized for large-scale applications.

## Contribution

The paper defines Det-Dec-POMDPs and develops IDPP, a novel scalable solver tailored for large deterministic multi-agent planning problems.

## Key findings

- IDPP efficiently solves large-scale Det-Dec-POMDPs.
- Det-Dec-POMDPs effectively model multi-robot navigation tasks.
- IDPP outperforms existing methods in scalability and efficiency.

## Abstract

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.