Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session L28: Low-Order Modeling: Applications
8:00 AM–10:36 AM,
Monday, November 20, 2023
Room: 152A
Chair: Andrew Fox, University of Wisconsin-Madison
Abstract: L28.00009 : Deep Reinforcement Learning for Autonomous Navigation in Complex Flows*
9:44 AM–9:57 AM
Presenter:
Selim Mecanna
(École Centrale Marseille (IRPHE))
Authors:
Selim Mecanna
(École Centrale Marseille (IRPHE))
Aurore Loisy
(École Centrale Marseille (IRPHE))
Christophe Eloy
(École Centrale Marseille (IRPHE))
Collaboration:
C0PEP0D
This is especially the case if the only information that can be accessed are the local velocity
gradients. This is the sort of challenging task that a plankton is faced with while drifting in the
oceans, or that of a GPS-denied autonomous underwater vehicle (AUV).
Reinforcement learning has become a popular tool to solve navigation problems
with local flow information. So far, these studies are mostly proofs-of-concept. They
demonstrate that better-than-naive strategies can be learnt purely from experience.
But the next challenge for reinforcement learning is to discover truly smart strategies
that exploit the complex features of the underlying turbulent flow. This requires the
use of state-of-the-art reinforcement learning methods.
Here we explore this path to address the problem of directional navigation, we demonstrate
that DRL consistently matches or beat the performance of approximately optimal strategies
derived analytically. We also discuss the importance of using deep neural networks as opposed
to tabular policies. Overall, this study sheds light on the promising applications of reinforcement
learning in tackling directional navigation problems in turbulent environments.
*This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 834238).
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