IDQL generalizes IQL into an actor-critic framework and uses diffusion policies for robust policy extraction, outperforming prior offline RL methods.
Understanding the role of importance weighting for deep learning
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LiLAW learns to weight samples as easy, moderate or hard using three global scalars updated by one gradient step on a validation batch to improve noisy training performance.
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IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
IDQL generalizes IQL into an actor-critic framework and uses diffusion policies for robust policy extraction, outperforming prior offline RL methods.
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LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training
LiLAW learns to weight samples as easy, moderate or hard using three global scalars updated by one gradient step on a validation batch to improve noisy training performance.