Multi-Agent Reinforcement Learning Approach Scheduling for In-X Subnetworks

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  RILREW: Reinforcement Learning for Real-time Wireless Scheduling and Edge Caching: Theory and Algorithm Design

  Tirkkonen, O. (Principal investigator), Amidzade, M. (Project Member), Srinivasan, A. (Project Member), Singh, U. (Project Member), Shaikh, B. (Project Member) & Al-Tous, H. (Project Member)

  01/01/2022 → 31/12/2024

  Project: Academy of Finland: Other research funding

  • Cellular Network 100%
  • Long Short-Term Memory Network 100%
  • Markov Decision Process 90%
  • Deep Reinforcement Learning 89%
  • Wireless Networks 75%