论文标题
使用好奇心驱动的增强学习的积极双向飞行
Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learning
论文作者
论文摘要
在导航过程中，执行侵略性运动的能力对四肢运动很重要。但是，对实用应用的侵略性四极管飞行仍然是一个巨大的挑战。现有的积极飞行解决方案在很大程度上依赖于预定义的轨迹，这是一个耗时的预处理步骤。为了避免这种路径计划，我们提出了一种以好奇心驱动的加固学习方法，以进行积极的飞行任务，并引入了基于相似性的好奇模块，以加快训练程序。还采用了分支结构探索（BSE）策略来确保政策的鲁棒性，并确保可以直接在现实世界实验中执行模拟中训练的政策。模拟中的实验结果表明，我们的强化学习算法在积极的飞行任务中表现良好，加快了收敛过程并提高了政策的鲁棒性。此外，我们的算法显示了对真实可传递性令人满意的模拟，并且在现实世界实验中表现良好。
The ability to perform aggressive movements, which are called aggressive flights, is important for quadrotors during navigation. However, aggressive quadrotor flights are still a great challenge to practical applications. The existing solutions to aggressive flights heavily rely on a predefined trajectory, which is a time-consuming preprocessing step. To avoid such path planning, we propose a curiosity-driven reinforcement learning method for aggressive flight missions and a similarity-based curiosity module is introduced to speed up the training procedure. A branch structure exploration (BSE) strategy is also applied to guarantee the robustness of the policy and to ensure the policy trained in simulations can be performed in real-world experiments directly. The experimental results in simulations demonstrate that our reinforcement learning algorithm performs well in aggressive flight tasks, speeds up the convergence process and improves the robustness of the policy. Besides, our algorithm shows a satisfactory simulated to real transferability and performs well in real-world experiments.
