Dissecting Deep RL with High Update Ratios: Combatting Value Divergence

9 March 2024

Papers citing "Dissecting Deep RL with High Update Ratios: Combatting Value Divergence"

8 / 8 papers shown

Title
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL C. Voelcker Marcel Hussing Eric Eaton Amir-massoud Farahmand Igor Gilitschenski 41 1 0 11 Oct 2024
Normalization and effective learning rates in reinforcement learning Clare Lyle Zeyu Zheng Khimya Khetarpal James Martens H. V. Hasselt Razvan Pascanu Will Dabney 19 7 0 01 Jul 2024
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning Michal Nauman Michal Bortkiewicz Piotr Milo's Tomasz Trzciñski M. Ostaszewski Marek Cygan OffRL 30 17 0 01 Mar 2024
Disentangling the Causes of Plasticity Loss in Neural Networks Clare Lyle Zeyu Zheng Khimya Khetarpal H. V. Hasselt Razvan Pascanu James Martens Will Dabney AI4CE 55 32 0 29 Feb 2024
The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin Max Schwarzer P. DÓro Pierre-Luc Bacon Rameswar Panda OnRL 96 180 0 16 May 2022
Is High Variance Unavoidable in RL? A Case Study in Continuous Control Johan Bjorck Carla P. Gomes Kilian Q. Weinberger 65 23 0 21 Oct 2021
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods Baturay Saglam Enes Duran Dogan C. Cicek Furkan B. Mutlu Suleyman Serdar Kozat OffRL 37 12 0 22 Sep 2021
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics Arsenii Kuznetsov Pavel Shvechikov Alexander Grishin Dmitry Vetrov 136 187 0 08 May 2020