Reinforcement learning of optimal active particle navigation

• URL: http://arxiv.org/abs/2202.00812v1
• Date: Tue, 1 Feb 2022 23:47:59 GMT
• Title: Reinforcement learning of optimal active particle navigation
• Authors: Mahdi Nasiri, Benno Liebchen
• Abstract summary: We develop a machine learning-based approach that allows us to determine the gradientally optimal path of a self-propelled agent. Our method hinges on policy-based deep learning reinforcement techniques and, crucially, does not require any reward shaping or calculates. The presented method provides a powerful alternative to current analytical methods and opens a route towards a universal path planner for future intelligent particles.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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