# Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information

arXiv: https://arxiv.org/abs/2506.07829 · HTML: https://mlsanity.com/paper/2506.07829/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning](https://mlsanity.com/paper/2010.03950/) (2020)
- [Joint Inference of Reward Machines and Policies for Reinforcement Learning](https://mlsanity.com/paper/1909.05912/) (2019)
- [Reward Machines for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2007.01962/) (2020)
- [Reinforcement Learning with Temporal-Logic-Based Causal Diagrams](https://mlsanity.com/paper/2306.13732/) (2023)
- [Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning](https://mlsanity.com/paper/2307.05209/) (2023)

## Abstract (arXiv)

Reinforcement learning (RL) algorithms can find an optimal policy for a single agent to accomplish a particular task. However, many real-world problems require multiple agents to collaborate in order to achieve a common goal. For example, a robot executing a task in a warehouse may require the assistance of a drone to retrieve items from high shelves. In Decentralized Multi-Agent RL (DMARL), agents learn independently and then combine their policies at execution time, but often must satisfy constraints on compatibility of local policies to ensure that they can achieve the global task when combined. In this paper, we study how providing high-level symbolic knowledge to agents can help address unique challenges of this setting, such as privacy constraints, communication limitations, and performance concerns. In particular, we extend the formal tools used to check the compatibility of local policies with the team task, making decentralized training with theoretical guarantees usable in more scenarios. Furthermore, we empirically demonstrate that symbolic knowledge about the temporal evolution of events in the environment can significantly expedite the learning process in DMARL.