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Demystifying autonomous marine navigation with reinforcement learning

Published on:

30 July 2023

Primary Category:


Paper Authors:

Xi Lin,

John McConnell,

Brendan Englot


Key Details

Proposes distributional RL method to handle uncertainty in marine navigation

Learns policies that adaptively balance speed and risk avoidance

Outperforms classical algorithms in challenging simulated environments

Converges to smooth, efficient trajectories that maintain safe distances

Open-sources code and simulation environments for research

AI generated summary

Demystifying autonomous marine navigation with reinforcement learning

This paper proposes a reinforcement learning approach to enable autonomous navigation for unmanned surface vehicles in challenging marine environments with unknown currents and obstacles. The method learns to plan safe, efficient trajectories by interacting with simulated environments. Compared to classical algorithms, the learned policies are more robust to disturbances and avoid collisions more successfully.

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