The paper introduces Space-sampled Value Decay (SsVD), a forgetting mechanism designed for non-stationary reinforcement learning where the environment can drift at every timestep. SsVD selectively pulls value estimates of randomly chosen state-space elements toward a baseline, discarding outdated information without requiring reset or change-point detection. Integrated into Soft Actor Critic and Deep Q-Networks, SsVD outperforms its base algorithms across six non-stationary environments and can also promote optimism in hard-exploration tasks.
By Felix St\"orck, Philipp Hartmann, Fabian Hinder, Klaus Neumann, Barbara Hammer
arXiv:2511. 03836v2 Announce Type: replace Abstract: Deep Q-Networks (DQNs) estimate future returns by learning from transitions sampled from a replay buffer.
By Lipeng Zu, Hansong Zhou, Xiaonan Zhang
arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.
By Mao-Lin Luo, Zhe-Xu Wang, Zi-Hao Zhou, Bo Ye, Jian Zhao, Min-Ling Zhang, Tong Wei
arXiv:2603. 11395v3 Announce Type: replace-cross Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks.
By Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst, Luke Yang, Levin Kuhlmann, Gideon Kowadlo
arXiv:2606. 00350v1 Announce Type: cross Abstract: Offline reinforcement learning requires improving a policy from fixed data while avoiding out-of-distribution actions with unreliable value estimates.
By Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh, Scott Fujimoto, Hsiu-Chin Lin, David Meger
arXiv:2410.14606v3 Announce Type: replace
Abstract: Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streami...
By Mohamed Elsayed, Elena Sorina Lupu, Gautham Vasan, A. Rupam Mahmood