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

arXiv: https://arxiv.org/abs/2304.04346 · HTML: https://mlsanity.com/paper/2304.04346/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [Approximate information state for approximate planning and reinforcement learning in partially observed systems](https://mlsanity.com/paper/2010.08843/) (2020)
- [Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1811.01458/) (2018)
- [Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes](https://mlsanity.com/paper/1302.1525/) (2013)
- [Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.12603/) (2021)
- [Online Planning for Decentralized Stochastic Control with Partial History Sharing](https://mlsanity.com/paper/1908.02357/) (2019)
- [A Bilinear Programming Approach for Multiagent Planning](https://mlsanity.com/paper/1401.3461/) (2014)

## 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.