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Decentralized Coordination in Partially Observable Queueing Networks

arXiv 2208.13621 ↗First posted 29 Aug 2022cs.LG cs.DC cs.MAcited by 0

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Abstract (arXiv)

We consider communication in a fully cooperative multi-agent system, where the agents have partial observation of the environment and must act jointly to maximize the overall reward. We have a discrete-time queueing network where agents route packets to queues based only on the partial information of the current queue lengths. The queues have limited buffer capacity, so packet drops happen when they are sent to a full queue. In this work, we implemented a communication channel for the agents to share their information in order to reduce the packet drop rate. For efficient information sharing we use an attention-based communication model, called ATVC, to select informative messages from other agents. The agents then infer the state of queues using a combination of the variational auto-encoder, VAE, and product-of-experts, PoE, model. Ultimately, the agents learn what they need to communicate and with whom, instead of communicating all the time with everyone. We also show empirically that ATVC is able to infer the true state of the queues and leads to a policy which outperforms existing baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 29 Aug 2022.

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24 of 35 references.

References · 24

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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12 more
  1. 2016Learning Multiagent Communication with Backpropagationcited by 366
  2. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  3. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
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  7. 2017Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Gamescited by 96
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