Decentralized DDQN agents learn a slotted ALOHA-like access strategy that adapts to network conditions and reaches near-theoretical efficiency with fairness in simulations.
Multi-task reinforcement learning-based multiple access for dynamic wireless networks.IEEE Transactions on Mobile Computing, 24(9):9153–9167, 2025
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KISS: Keeping it Simple and Slotted when Learning to Communicate over Wireless
Decentralized DDQN agents learn a slotted ALOHA-like access strategy that adapts to network conditions and reaches near-theoretical efficiency with fairness in simulations.