论文标题
最佳主动粒子导航的强化学习
Reinforcement learning of optimal active particle navigation
论文作者
论文摘要
在微观和纳米级的自螺旋颗粒的发展为未来在活性物理，显微外科手术和靶向药物递送中应用的巨大潜力引发了巨大的潜力。但是，尽管后一个应用程序会引起有关如何最佳导航到目标的任务，例如一个癌细胞，仍然尚无简单的方法来确定足够复杂的环境中的最佳途径。在这里，我们开发了一种基于机器学习的方法，该方法使我们首次确定自propelled剂的渐近最佳路径，该路径可以自由地转向复杂的环境。我们的方法取决于基于政策梯度的深入强化学习技术，并且至关重要的是，不需要任何奖励塑造或启发式方法。提出的方法为当前的分析方法提供了一种强大的替代方法，以计算最佳轨迹，并为未来的智能活动粒子打开通用路径计划器的途径。
The development of self-propelled particles at the micro- and the nanoscale has sparked a huge potential for future applications in active matter physics, microsurgery, and targeted drug delivery. However, while the latter applications provoke the quest on how to optimally navigate towards a target, such as e.g. a cancer cell, there is still no simple way known to determine the optimal route in sufficiently complex environments. Here we develop a machine learning-based approach that allows us, for the first time, to determine the asymptotically optimal path of a self-propelled agent which can freely steer in complex environments. Our method hinges on policy gradient-based deep reinforcement learning techniques and, crucially, does not require any reward shaping or heuristics. The presented method provides a powerful alternative to current analytical methods to calculate optimal trajectories and opens a route towards a universal path planner for future intelligent active particles.
