Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning

by   Michele Buzzicotti, et al.
Johns Hopkins University

We present theoretical and numerical results concerning the problem to find the path that minimizes the time to navigate between two given points in a complex fluid under realistic navigation constraints. We contrast deterministic Optimal Navigation (ON) control with stochastic policies obtained by Reinforcement Learning (RL) algorithms. We show that Actor-Critic RL algorithms are able to find quasi-optimal solutions in the presence of either time-independent or chaotically evolving flow configurations. For our application, ON solutions develop unstable behavior within the typical duration of the navigation process, and are therefore not useful in practice. We first explore navigation of turbulent flow using a constant propulsion speed. Based on a discretized phase-space, the propulsion direction is adjusted with the aim to minimize the time spent to reach the target. Further, we explore a case where additional control is obtained by allowing the engine to power off. Exploiting advection of the underlying flow, allows the target to be reached with less energy consumption. In this case, we optimize a linear combination between the total navigation time and the total time the engine is switched off. Our approach can be generalized to other setups, for example, navigation under imperfect environmental forecast or with different models for the moving vessel.


page 2

page 5

page 7


Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning

To find the path that minimizes the time to navigate between two given p...

Learning Efficient Navigation in Vortical Flow Fields

Efficient point-to-point navigation in the presence of a background flow...

Stochastic optimal well control in subsurface reservoirs using reinforcement learning

We present a case study of model-free reinforcement learning (RL) framew...

Robust Unmanned Surface Vehicle Navigation with Distributional Reinforcement Learning

Autonomous navigation of Unmanned Surface Vehicles (USV) in marine envir...

Reinforcement Learning for Active Flow Control in Experiments

We demonstrate experimentally the feasibility of applying reinforcement ...

Stabilising viscous extensional flows using Reinforcement Learning

The four-roll mill, wherein four identical cylinders undergo rotation of...

Please sign up or login with your details

Forgot password? Click here to reset