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A Novel Point-based Algorithm for Multi-agent Control Using the Common Information Approach

arXiv 2304.04346 ↗First posted 10 Apr 2023cs.AI cs.MA cs.SYcited by 0

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Abstract (arXiv)

The Common Information (CI) approach provides a systematic way to transform a multi-agent stochastic control problem to a single-agent partially observed Markov decision problem (POMDP) called the coordinator's POMDP. However, such a POMDP can be hard to solve due to its extraordinarily large action space. We propose a new algorithm for multi-agent stochastic control problems, called coordinator's heuristic search value iteration (CHSVI), that combines the CI approach and point-based POMDP algorithms for large action spaces. We demonstrate the algorithm through optimally solving several benchmark problems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 41 references that are in the map. First posted on arXiv 10 Apr 2023.

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References · 6

  1. 2018Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learningcited by 67
  2. 2020Approximate information state for approximate planning and reinforcement learning in partially observed systemscited by 67
  3. 2013Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processescited by 24
  4. 2021Common Information based Approximate State Representations in Multi-Agent Reinforcement Learningcited by 8
  5. 2019Online Planning for Decentralized Stochastic Control with Partial History Sharingcited by 5
  6. 2014A Bilinear Programming Approach for Multiagent Planningcited by 1

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