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Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information

arXiv 2506.07829 ↗First posted 9 Jun 2025cs.LG cs.AI cs.MAcited by 0

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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.

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.

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References · 5

  1. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 191
  2. 2019Joint Inference of Reward Machines and Policies for Reinforcement Learningcited by 56
  3. 2020Reward Machines for Cooperative Multi-Agent Reinforcement Learningcited by 35
  4. 2023Reinforcement Learning with Temporal-Logic-Based Causal Diagramscited by 2
  5. 2023Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learningcited by 1

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  1. 2025Contextual Knowledge Sharing in Multi-Agent Reinforcement Learning with Decentralized Communication and Coordination
  2. 2025A Survey of Multi Agent Reinforcement Learning: Federated Learning and Cooperative and Noncooperative Decentralized Regimes
  3. 2025Multi-agent In-context Coordination via Decentralized Memory Retrieval
  4. 2025Explaining Decentralized Multi-Agent Reinforcement Learning Policies
  5. 2022On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios
  6. 2025A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference
  7. 2024Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey
  8. 2026HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation