Scaling CrossQ with Weight Normalization

Integrated architectures for learning, planning, and reacting based on approximating dynamic programming

R. S. Sutton · 1990

Earlier work this paper cites.

Mujoco: A physics engine for model-based control

E. Todorov, T. Erez, and Y. Tassa · 2012

Earlier work this paper cites.

Markov decision processes: discrete stochastic dynamic programming

M. L. Puterman · 2014

Earlier work this paper cites.

L2 regularization versus batch and weight normalization

Original

T. Van Laarhoven · 2017

Earlier work this paper cites.

Reinforcement Learning: An Introduction

R. S. Sutton and A. G. Barto · 2018

Earlier work this paper cites.

When to trust your model: Model-based policy optimization

M. Janner, J. Fu, M. Zhang, and S. Levine · 2019

Earlier work this paper cites.

Deep reinforcement learning at the edge of the statistical precipice

R. Agarwal, M. Schwarzer, P. S. Castro, A. C. Courville, and M. Bellemare · 2021

Cited alongside, same era.

Randomized ensembled double Q-learning: Learning fast without a model

X. Chen, C. Wang, Z. Zhou, and K. Ross · 2021

Cited alongside, same era.

Dropout q-functions for doubly efficient reinforcement learning

T. Hiraoka, T. Imagawa, T. Hashimoto, T. Onishi, and Y. Tsuruoka · 2021

Cited alongside, same era.

Samba: Safe model-based & active reinforcement learning

A. I. Cowen-Rivers, D. Palenicek, V. Moens, M. A. Abdullah, A. Sootla, J. Wang, and H. Bou-Ammar · 2022

Cited alongside, same era.

Sample-efficient reinforcement learning by breaking the replay ratio barrier

P. D’Oro, M. Schwarzer, E. Nikishin, P.-L. Bacon, M. G. Bellemare, and A. Courville · 2022

Cited alongside, same era.

The primacy bias in deep reinforcement learning

Normalization techniques in training dnns: Methodology, analysis and application

L. Huang, J. Qin, Y. Zhou, F. Zhu, L. Liu, and L. Shao · 2023

Later among the works it cites.

Efficient deep reinforcement learning requires regulating overfitting

Q. Li, A. Kumar, I. Kostrikov, and S. Levine · 2023

Later among the works it cites.

CrossQ: Batch normalization in deep reinforcement learning for greater sample efficiency and simplicity

A. Bhatt, D. Palenicek, B. Belousov, M. Argus, A. Amiranashvili, T. Brox, and J. Peters · 2024

Later among the works it cites.

Dissecting deep rl with high update ratios: Combatting value overestimation and divergence

Original

M. Hussing, C. Voelcker, I. Gilitschenski, A.-m. Farahmand, and E. Eaton · 2024

Later among the works it cites.

Normalization and effective learning rates in reinforcement learning

C. Lyle, Z. Zheng, K. Khetarpal, J. Martens, H. van Hasselt, R. Pascanu, and W. Dabney · 2024

Later among the works it cites.