Deep Reinforcement Learning for Dynamic Multichannel Access10/04/2017 Objective: Inspiration We consider the problem of dynamic multichannel access in a WSN. The problem can be formulated as a POMDP, which is intractable. As a solution, we implement a Deep Q-Network (DQN) that can work without any prior knowledge of the system dynamics. We show through simulations and a real testbed that DQN has the capability to learn an optimal or near-optimal policy in complex real scenarios. Speaker(s)
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