通过深度强化学习利用涡流进行高效集体游泳
Efficient collective swimming by harnessing vortices through deep reinforcement learning
摘要：
鱼群在其同伴的涡流尾流中通过充满机械能的复杂流场。他们的上学行为与进化优势有关，包括集体节能。鱼类如何从其复杂的流体环境中获取能量，以及在集体游泳过程中控制能量提取的潜在物理机制仍然是未知的。在这里，我们表明，鱼可以提高他们的持续推进效率，积极遵循，明智地拦截，涡流后，其他游泳者。这种游泳策略导致集体节能，并通过深度强化学习与高保真流动模拟的首次结合来揭示。我们发现，一个“聪明的游泳者”可以调整其位置和身体变形，以同步与迎面而来的涡流的动量，提高其平均游泳效率，而无需花费的领导者。结果表明，鱼类可能会收获的能量存储在涡流产生的同行，并支持的猜想，形成游泳是积极的优势。此外，这项研究表明，深度强化学习可以为复杂的流场产生导航算法，对自主机器人群的节能具有重要意义。
英文摘要：
Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behaviour has been associated with evolutionary advantages including collective energy savings. How fish harvest energy from their complex fluid environment and the underlying physical mechanisms governing energy-extraction during collective swimming, is still unknown. Here we show that fish can improve their sustained propulsive efficiency by actively following, and judiciously intercepting, vortices in the wake of other swimmers. This swimming strategy leads to collective energy-savings and is revealed through the first ever combination of deep reinforcement learning with high-fidelity flow simulations. We find that a `smart-swimmer' can adapt its position and body deformation to synchronise with the momentum of the oncoming vortices, improving its average swimming-efficiency at no cost to the leader. The results show that fish may harvest energy deposited in vortices produced by their peers, and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep reinforcement learning can produce navigation algorithms for complex flow-fields, with promising implications for energy savings in autonomous robotic swarms.
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