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New reinforcement learning method optimizes underwater vehicle power budgets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach to control underwater vehicles by integrating energy efficiency directly into the reinforcement learning process. This method formulates control as a constrained Markov decision process, allowing for an explicit power budget to be set in physical units. A PPO-Lagrangian algorithm is used to solve this, with a dual variable updated online to meet the budget for each specific vehicle and task. Testing in the MarineGym simulator demonstrated significant power reductions of 14-65% across various vehicles and tasks compared to a task-accuracy-only baseline, while maintaining smooth operation and task accuracy. AI

IMPACT This research offers a tuning-free method for energy-efficient control of underwater vehicles, potentially extending mission ranges and endurance.

RANK_REASON The cluster contains a research paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New reinforcement learning method optimizes underwater vehicle power budgets

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The cluster contains a research paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yinuo Wang, Gavin Tao, Yuze Liu, John V. Ringwood ·

    Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning

    arXiv:2606.25680v2 Announce Type: replace-cross Abstract: Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing less thruster power directly extends mission range and endurance. Reinf…

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