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Online Reinforcement Learning involves learning policies through continuous interaction with the environment, adapting to changes in real-time.
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 Online Pre-Training for Offline-to-Online Reinforcement Learning Yongjae Shin Jeonghye Kim Whiyoung Jung Sunghoon Hong Deunsol Yoon ...Geonhyeong Kim Jongseong Chae Youngchul Sung Kanghoon Lee Woohyung Lim | |||
 MobileGUI-RL: Advancing Mobile GUI Agent through Reinforcement Learning in Online Environment Yucheng Shi Wenhao Yu Zaitang Li Yonglin Wang Hongming Zhang Ninghao Liu Haitao Mi Dong Yu | |||
 Accelerated Online Reinforcement Learning using Auxiliary Start State Distributions Aman Mehra Alexandre Capone Jeff Schneider | |||
 SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training Mingdong Wu Lehong Wu Yizhuo Wu Weiyao Huang Hongwei Fan ...Jinzhou Li Jiahe Ying Long Yang Yuanpei Chen Hao Dong | |||
 PNAct: Crafting Backdoor Attacks in Safe Reinforcement LearningInternational Joint Conference on Artificial Intelligence (IJCAI), 2025 Weiran Guo Guanjun Liu Ziyuan Zhou Ling Wang | |||
 Reliability-Adjusted Prioritized Experience Replay Leonard S. Pleiss Tobias Sutter Maximilian Schiffer | |||
 Pre-training for Recommendation UnlearningAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2025 | |||
 Designing Pin-pression Gripper and Learning its Dexterous Grasping with Online In-hand AdjustmentACM Transactions on Graphics (TOG), 2025 | |||
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