Figure ten displays the coaching curve of the proposed DQN-centered UAV detouring algorithm when you'll find thirty sensors and six hurdles. We are able to see that the benefits slowly greater from the start as the number of training episodes enhanced. The general reward grows noticeably following the 4000th episode from the 7000 in overall. This transpires because the agent utilizes chance to randomly examine new positions at the outset, which might not deliver the most beneficial benefits.
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For Each and every episode, the algorithm sets the Preliminary state and iterates Just about every step, exactly where the UAV’s existing state is updated, and an motion is chosen according to a plan including the ϵ
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Then, a deep reinforcement Mastering-based algorithm, named the DQN-based mostly UAV detouring algorithm, is proposed to enable the UAV for making clever detour conclusions in the constrained surroundings. The UAV have to finish its tour (data collection and recharging sensors) with no exceeding its battery capability, ensuring Every sensor has the minimal residual Vitality and consuming Vitality for transmitting and producing data, following getting recharged with the UAV at the end of the tour. Lastly, simulation results demonstrate the success of your proposed DQN-dependent UAV detouring algorithm in data collection and recharging the sensors when minimizing the whole Power consumption of your UAV. In comparison to other baseline algorithm variants, the proposed algorithm outperforms all of these.
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