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