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IEEE/ICACT20230128 Question.2 Questioner: haodp@dau.edu.vn 2023-02-12 오전 3:01:33	IEEE/ICACT20230128 Answer.2 Answer by Auhor p2109422@mpu.edu.mo 2023-02-12 오전 3:01:33
What are the trade-offs between using heuristic algorithms and reinforcement learning for path planning for cellular connected UAVs, and how can these trade-offs be balanced in practice?	According to our experiments, the heuristic algorithm performs better in terms of correctness and the advantage increases as the number of cities to be visited increases. At the same time, the reinforcement learning method is unable to handle the problem when the number of cities is large. We demonstrated the correctness of RL when the number of cities was equal to 50 and the result was unacceptable. Furthermore, RL cannot handle changes in the environment, so a new model has to be trained for each different environment. On the other hand, RL can search for a path without a distance matrix and its search time is faster than that of the heuristic algorithm, which saves computer resources and time during path planning. We suggest using RL only when the number of cities is less than 25.

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