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A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning

arXiv 2202.10612 ↗First posted 22 Feb 2022cs.MA cs.AIcited by 0

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Abstract (arXiv)

We propose a model enabling decentralized multiple agents to share their perception of environment in a fair and adaptive way. In our model, both the current message and historical observation are taken into account, and they are handled in the same recurrent model but in different forms. We present a dual-level recurrent communication framework for multi-agent systems, in which the first recurrence occurs in the communication sequence and is used to transmit communication data among agents, while the second recurrence is based on the time sequence and combines the historical observations for each agent. The developed communication flow separates communication messages from memories but allows agents to share their historical observations by the dual-level recurrence. This design makes agents adapt to changeable communication objects, while the communication results are fair to these agents. We provide a sufficient discussion about our method in both partially observable and fully observable environments. The results of several experiments suggest our method outperforms the existing decentralized communication frameworks and the corresponding centralized training method.

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References · 16

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  3. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  4. 2016Learning Multiagent Communication with Backpropagationcited by 355
  5. 2018Actor-Attention-Critic for Multi-Agent Reinforcement Learningcited by 259
  6. 2018TarMAC: Targeted Multi-Agent Communicationcited by 201
  7. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 170
  8. 2020When2com: Multi-Agent Perception via Communication Graph Groupingcited by 157
  9. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  10. 2017Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Gamescited by 93
  11. 2019Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learningcited by 33
  12. 2018Managing engineering systems with large state and action spaces through deep reinforcement learningcited by 32
4 more
  1. 2016Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networkscited by 21
  2. 2020Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learningcited by 13
  3. 2020Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computingcited by 13
  4. 2018Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learningcited by 12

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