D2RL: Deep Dense Architectures in Reinforcement Learning

D2RL: Deep Dense Architectures in Reinforcement Learning

19 October 2020

Homanga Bharadhwaj

Papers citing "D2RL: Deep Dense Architectures in Reinforcement Learning"

7 / 7 papers shown

Title
DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands Ritvik Singh Arthur Allshire Ankur Handa Nathan D. Ratliff Karl Van Wyk 72 7 0 27 Nov 2024
Shrink-Perturb Improves Architecture Mixing during Population Based Training for Neural Architecture Search A. Chebykin A. Dushatskiy T. Alderliesten Peter A. N. Bosman 14 0 0 28 Jul 2023
Bigger, Better, Faster: Human-level Atari with human-level efficiency Max Schwarzer J. Obando-Ceron Aaron C. Courville Marc G. Bellemare Rishabh Agarwal P. S. Castro OffRL 29 81 0 30 May 2023
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo Haque Ishfaq Qingfeng Lan Pan Xu A. R. Mahmood Doina Precup Anima Anandkumar Kamyar Azizzadenesheli BDL OffRL 13 20 0 29 May 2023
Revisiting the Minimalist Approach to Offline Reinforcement Learning Denis Tarasov Vladislav Kurenkov Alexander Nikulin Sergey Kolesnikov OffRL 20 35 0 16 May 2023
Hierarchical Skills for Efficient Exploration Jonas Gehring Gabriel Synnaeve Andreas Krause Nicolas Usunier 11 40 0 20 Oct 2021
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning Samarth Sinha Ajay Mandlekar Animesh Garg OffRL 11 104 0 10 Mar 2021