A Path Planning Method for Fire Detection in Educational Experimental Zones Based on an Improved Deep Q-learning Algorithm
This paper proposes an improved Deep Q-Learning algorithm for UAV fire detection in educational experimental zones that integrates multi-objective reward optimization, diagonal motion incentives, and A*-inspired constraints to significantly enhance path efficiency, reduce risk exposure, and improve mission success rates compared to conventional methods.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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