The Effectiveness of World Models for Continual Reinforcement Learning

29 November 2022

Papers citing "The Effectiveness of World Models for Continual Reinforcement Learning"

8 / 8 papers shown

Title
Knowledge Retention for Continual Model-Based Reinforcement Learning Yixiang Sun Haotian Fu M. L. Littman G. Konidaris OffRL CLL VLM 47 0 0 06 Mar 2025
Towards Unraveling and Improving Generalization in World Models Qiaoyi Fang Weiyu Du Hang Wang Junshan Zhang OOD 28 0 0 03 Jan 2025
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement Zhi Wang Li Lyna Zhang Wenhao Wu Yuanheng Zhu Dongbin Zhao C. L. Philip Chen OffRL 33 6 0 15 Oct 2024
Augmenting Replay in World Models for Continual Reinforcement Learning Luke Yang L. Kuhlmann Gideon Kowadlo VLM KELM CLL OffRL 14 0 0 30 Jan 2024
Multi-agent Continual Coordination via Progressive Task Contextualization Lei Yuan Lihe Li Ziqian Zhang Fuxiang Zhang Cong Guan Yang Yu CLL 29 7 0 07 May 2023
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research Mikayel Samvelyan Robert Kirk Vitaly Kurin Jack Parker-Holder Minqi Jiang Eric Hambro Fabio Petroni Heinrich Küttler Edward Grefenstette Tim Rocktaschel OffRL 226 89 0 27 Sep 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,652 0 05 Dec 2016
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi Zhourong Chen Hao Wang Dit-Yan Yeung W. Wong W. Woo 201 7,884 0 13 Jun 2015