Technology
Constructing Fast Network through Deconstruction of Convolution
Neural Information Processing Systems http://nips.cc/
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
Vikash Goel, Jameson Weng, Pascal Poupart
Neural Information Processing Systems http://nips.cc/
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang
Model-free deepreinforcement learning (RL)algorithms havebeenwidely used for a range of complex control tasks.
bb1443cc31d7396bf73e7858cea114e1-AuthorFeedback.pdf
But in the field of reinforcement learning (RL),L2-norm is the most common choice due to its5 efficiency and effectiveness. Thus we adoptL2-norm in the paper to ensure consistency between the objective of6 Andersonacceleration(AA)andthelossofQ-valuefunction(critic).7 Minors.
HIQL: Offline Goal-Conditioned RL with Latent States as Actions Seohong Park
We observe that part of this difficulty stems from the "signal-to-noise"