# Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach

arXiv: https://arxiv.org/abs/1707.06334 · HTML: https://mlsanity.com/paper/1707.06334/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [Select2Col: Leveraging Spatial-Temporal Importance of Semantic Information for Efficient Collaborative Perception](https://mlsanity.com/paper/2307.16517/) (2023)
- [Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management](https://mlsanity.com/paper/2212.07684/) (2022)
- [Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments](https://mlsanity.com/paper/2110.11006/) (2021)
- [Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss](https://mlsanity.com/paper/2201.06619/) (2022)
- [Human Machine Co-Adaptation Model and Its Convergence Analysis](https://mlsanity.com/paper/2503.07319/) (2025)

## Abstract (arXiv)

Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination. In some settings, the structure of a problem allows a distributed solution with limited communication. Here, we consider a scenario where no communication is available, and instead we learn local policies for all agents that collectively mimic the solution to a centralized multi-agent static optimization problem. Our main contribution is an information theoretic framework based on rate distortion theory which facilitates analysis of how well the resulting fully decentralized policies are able to reconstruct the optimal solution. Moreover, this framework provides a natural extension that addresses which nodes an agent should communicate with to improve the performance of its individual policy.