A behavior-constrained RL framework with receding-horizon credit assignment learns high-performance control policies that stay aligned with expert behavior in race car simulation.
URL http: //arxiv.org/abs/2006.06412
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A new spatial-spectral adaptive fidelity and noise prior reduction framework for hyperspectral image denoising uses an adaptive weight tensor and representative coefficient total variation to handle mixed noise with superior performance and efficiency.
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Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
A behavior-constrained RL framework with receding-horizon credit assignment learns high-performance control policies that stay aligned with expert behavior in race car simulation.
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Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising
A new spatial-spectral adaptive fidelity and noise prior reduction framework for hyperspectral image denoising uses an adaptive weight tensor and representative coefficient total variation to handle mixed noise with superior performance and efficiency.