FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
Reinforcement learning in robotics: A survey.The International Journal of Robotics Research, 32(11):1238–1274
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Weighted BC estimates trajectory density ratios from a clean reference set via binary discrimination and reweights the BC loss to converge to the clean expert policy with finite-sample bounds independent of contamination rate.
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FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control
FlashSAC improves training speed and final performance of off-policy RL on high-dimensional robot tasks by reducing update frequency, increasing model scale, and bounding norms to limit critic error accumulation.
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Density-Ratio Weighted Behavioral Cloning: Learning Control Policies from Corrupted Datasets
Weighted BC estimates trajectory density ratios from a clean reference set via binary discrimination and reweights the BC loss to converge to the clean expert policy with finite-sample bounds independent of contamination rate.