data for UAV Path Planning Based on Proximal Policy Optimization Algorithm with Long Short-Term Memory Networks and Generalized Integral Compensator

This study proposes a more competitive and sample-efficient algorithm: Memory-GIC-PPO, specifically to address POMDPs in UAV path planning. The effectiveness of the proposed algorithm is thoroughly evaluated through simulations conducted on the Airsim platform. The results convincingly demonstrate that Memory-GIC-PPO enables the UAV to achieve optimal path planning in complex environments and outperforms the benchmark algorithms in terms of sampling efficiency and success rates. This dataset contains the rewards obtained by the Memory-GIC-PPO, LSTM-TD3, and PPO algorithms in the designed scenarios, as well as their respective success rates. In addition, the rewards and success rates associated with the results of the ablation experiments were also demonstrated in the dataset.