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Amid them, the proposed DQN-based mostly UAV detouring algorithm obtains the lowest total Power intake for that UAV in all situations, which can be found in Figure 6a,b, Determine 7a,b and Figure 8a,b. The instruction curve sooner or later flattens out, signifying that the proposed DQN-primarily based UAV detouring algorithm has uncovered an answer or possibly a list of virtually optimum solutions. At this stage, additional training won't produce significant enhancements, and also the product persistently identifies paths which might be as quick as is possible. The slope might fluctuate a little as the program balances concerning exploitation and exploration. All round, the development signifies a drop in the overall route Value, demonstrating the DQN’s efficacy in resolving the TSP. In addition, the craze displays a lower in the full Vitality usage with the UAV, highlighting the effectiveness on the proposed DQN-centered UAV detouring algorithm in addressing the DOCEM issue.
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