论文标题
通过课程学习，室内路径计划多个无人驾驶汽车
Indoor Path Planning for Multiple Unmanned Aerial Vehicles via Curriculum Learning
论文作者
论文摘要
在这项研究中，对两辆无人机（UAV）的室内路径规划进行了多机构增强学习。每个无人机都执行了尽可能快地从随机配对的初始位置到具有障碍的环境中的目标位置的任务。为了最大程度地减少训练时间并防止无人机的损害，通过模拟进行学习。考虑到多代理环境的非平稳特征，其中最佳行为会根据其他代理的作用而变化，因此其他无人机的作用也包括在每个无人机的状态空间中。课程学习是在两个阶段进行的，以提高学习效率。与获得73.6％和79.9％的其他学习策略相比，获得的目标率为89.0％。
Multi-agent reinforcement learning was performed in this study for indoor path planning of two unmanned aerial vehicles (UAVs). Each UAV performed the task of moving as fast as possible from a randomly paired initial position to a goal position in an environment with obstacles. To minimize training time and prevent the damage of UAVs, learning was performed by simulation. Considering the non-stationary characteristics of the multi-agent environment wherein the optimal behavior varies based on the actions of other agents, the action of the other UAV was also included in the state space of each UAV. Curriculum learning was performed in two stages to increase learning efficiency. A goal rate of 89.0% was obtained compared with other learning strategies that obtained goal rates of 73.6% and 79.9%.
