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Reinforcement learning uses symmetry and data augmentation for faster aircraft control

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for offline reinforcement learning that leverages the symmetry of dynamical systems to improve sample efficiency. This approach uses symmetric data augmentation to enhance the state-action space coverage within the Deep Deterministic Policy Gradient algorithm. A dual-critic structure, with one critic trained on augmented samples, further boosts sample utilization, leading to faster policy convergence in simulations, particularly for aircraft attitude control. AI

IMPACT Introduces a novel data augmentation technique for reinforcement learning that could improve sample efficiency in control systems.

RANK_REASON This is a research paper detailing a novel algorithm for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Reinforcement learning uses symmetry and data augmentation for faster aircraft control

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This is a research paper detailing a novel algorithm for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yifei Li, Erik-Jan van Kampen ·

    Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft

    arXiv:2407.11077v4 Announce Type: replace Abstract: The symmetry of dynamical systems can be exploited for state-transition prediction and to facilitate control policy optimization. This paper leverages system symmetry to develop sample-efficient offline reinforcement learning (R…

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