Reinforcement learning uses symmetry and data augmentation for faster aircraft control

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

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

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

在 arXiv cs.LG 阅读 →

• Markov Decision Process
• Yifei Li
• Deep Deterministic Policy Gradient

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