SoftGAC defines a stochastic bridge from base to action latent that converts the MaxEnt objective into a tractable relative-entropy term reducible to control energy, achieving competitive returns with one-pass sampling.
CrossQ: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity.International Conference on Learning Representations (ICLR)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
XQCfD accelerates actor-critic RL by using prior data, pretrained policies, and stationary architectures to achieve state-of-the-art results on Adroit, Robomimic, and MimicGen manipulation benchmarks with low update-to-data ratios.
citing papers explorer
-
Generative Actor-Critic with Soft Bridge Policies
SoftGAC defines a stochastic bridge from base to action latent that converts the MaxEnt objective into a tractable relative-entropy term reducible to control energy, achieving competitive returns with one-pass sampling.
-
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
XQCfD accelerates actor-critic RL by using prior data, pretrained policies, and stationary architectures to achieve state-of-the-art results on Adroit, Robomimic, and MimicGen manipulation benchmarks with low update-to-data ratios.