Implications of Decentralized Q-learning Resource Allocation in Wireless Networks

Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results. In this work we propose a stateless variation of Q-learning, which we apply to exploit spatial reuse in a wireless network... (read more)

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