Work

Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics

Year: 2026

Type: other

Abstract: Many high-level multi-agent planning problems, such as multi-robot navigation and path planning, can be modeled with deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs)—a subclass of Dec-POMDPs with deterministic transitions and observations given the state and joint actions. We then propose a practical solver, Iterative Deterministic POMDP Planning (IDPP), based on the classic Joint Equilibrium Search for Policies framework, specifically optimized to handle large-scale Det-Dec-POMDPs that existing Dec-POMDP solvers cannot handle efficiently.

Source: Underline Science Inc.

DOI: 10.48448/bsqt-as74

Authors: Association for Artificial Intelligence 2026, Hawes, Nick, Lacerda, Bruno, li, zhikun, Schutz, Alex, Skilton, Robert, You, Yang

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7128699544

Cites:

Cited by:

Related to: 10