The content discusses the utilization of equivariant ensembles and regularization in reinforcement learning to exploit environmental symmetries. It introduces the concept of constructing equivariant policies and invariant value functions without specialized neural network components, showcasing benefits in sample efficiency and performance. The paper focuses on a map-based path planning case study involving UAV coverage path planning, demonstrating how these techniques improve training efficiency, robustness, and performance. The methodology section explains the construction of equivariant ensembles to enforce equivariance and invariance of policies and value functions without special neural network designs. It also delves into the concept of equivariant regularization to add inductive bias during training. The experiment section details the setup, training acceleration, performance comparison on different sets of maps (in-distribution, rotated in-distribution, out-of-distribution), and examines equivariance through regularization. The discussion highlights future research directions and considerations for real-world applications.
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by Mirco Theile... kl. arxiv.org 03-20-2024
https://arxiv.org/pdf/2403.12856.pdfDybere Forespørgsler