Figure ten reveals the education curve from the proposed DQN-based mostly UAV detouring algorithm when you will discover thirty sensors and six obstacles. We can see the rewards step by step greater from the beginning as the amount of instruction episodes increased. The overall reward grows significantly once the 4000th episode from your 7000 in whole. This takes place since the agent utilizes probability to randomly take a look at new positions in the beginning, which might not generate the ideal results.
, respectively. An agent have to go to Every single sensor by its respective deadline though The existing state is constructed. According to the proposed DQN-centered UAV detouring algorithm, an episode corresponds to 1 instance in the UAV sensor going to tour (similar to a TSP tour), which incorporates all sensors without the need of repetition.
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The results with the proposed DQN-centered Remedy were being in comparison with other condition-of-the-artwork algorithms: the GA, ACO, as well as a Greedy algorithm. The comparison showed the DQN-centered UAV detouring algorithm persistently outperformed the choices in vital general performance metrics, such as total energy intake with the UAV, typical arrival time when different the amount of sensors and hurdles, along with many deadlines for arriving on the sensors.
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The state with the agent includes your complete graph connecting all sensors, the sensors’ coordinates, as effectively given that the historical past of sensors frequented. The environment is completely observed and simple, allowing the agent for making deterministic moves along graph edges and to observe the gap traveled.
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