ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

8 December 2021

Papers citing "ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives"

4 / 4 papers shown

Title
Efficiently Quantifying Individual Agent Importance in Cooperative MARL Omayma Mahjoub Ruan de Kock Siddarth S. Singh Wiem Khlifi Abidine Vall Kale-ab Tessera Arnu Pretorius FAtt 85 2 0 13 Dec 2023
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning Siddarth S. Singh Omayma Mahjoub Ruan de Kock Wiem Khlifi Abidine Vall Kale-ab Tessera Arnu Pretorius 106 1 0 13 Dec 2023
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice Toshinori Kitamura Tadashi Kozuno Yunhao Tang Nino Vieillard Michal Valko ... Olivier Pietquin Matthieu Geist Csaba Szepesvári Wataru Kumagai Yutaka Matsuo OffRL 90 3 0 22 May 2023
Continual Reinforcement Learning with TELLA Neil Fendley Cash Costello Eric Q. Nguyen Gino Perrotta Corey Lowman CLL 58 2 0 08 Aug 2022