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Alex Schutz

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2 papers

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AAAI Conference 2026 Conference Paper

Scalable Solution Methods for Dec-POMDPs with Deterministic Dynamics

Yang You
Alex Schutz
Zhikun Li
Bruno Lacerda
Robert Skilton
Nick Hawes

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.

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IJCAI Conference 2025 Conference Paper

A Finite-State Controller Based Offline Solver for Deterministic POMDPs

Alex Schutz
Yang You
Matías Mattamala
Ipek Caliskanelli
Bruno Lacerda
Nick Hawes

Deterministic partially observable Markov decision processes (DetPOMDPs) often arise in planning problems where the agent is uncertain about its environmental state but can act and observe deterministically. In this paper, we propose DetMCVI, an adaptation of the Monte Carlo Value Iteration (MCVI) algorithm for DetPOMDPs, which builds policies in the form of finite-state controllers (FSCs). DetMCVI solves large problems with a high success rate, outperforming existing baselines for DetPOMDPs. We also verify the performance of the algorithm in a real-world mobile robot forest mapping scenario.

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