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 SsVD selectively pulls value estimates of randomly chosen state-space elements toward a baseline, discarding outdated information without requiring reset or change-point detection.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
The paper introduces TMLN (Trajectory-Modulatory Landscape Navigation), a method that treats continual learning as an optimal control problem on a curved loss landscape. It uses a diagonal empirical Fisher Information Matrix to approximate a local Riemannian manifold and dynamically modulates a preconditioner based on the network’s historical parameter trajectory. This trajectory‑based preconditioning is integrated into gradient updates to protect important parameter directions without adding explicit penalties, and experiments on class‑ and domain‑incremental benchmarks show a significant reduction in the loss barrier between tasks.
By Isabelle Aguilar, Zayn Andre Zainal, Luis Fernando Herbozo Contreras, Zhaojing Huang, Omid Kavehei
The paper investigates whether enhancing goal representations improves goal-conditioned reinforcement learning (GCRL) performance. By creating an exact temporal-distance goal representation in deterministic mazes and systematically degrading its geometric quality, the authors find that changes in goal representation have little effect on performance. In contrast, degrading the agent’s current state representation more than doubles failure rates, indicating that state representation is the critical bottleneck. The study further demonstrates that simple random Fourier positional encodings can significantly boost performance on challenging navigation tasks without additional map or objective modifications.
By Syed Nazmus Sakib, Abdul Monaf Chowdhury, Nafiul Haque, Shifat E Arman, Md Mehedi Hasan
arXiv:2605. 25170v2 Announce Type: replace-cross Abstract: Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models.
By Kordel K. France, Ovidiu Daescu
arXiv:2609.17141v1 Announce Type: cross
Abstract: Traversability prediction is a critical component of autonomous navigation in unstructured environments, where complex and uncertain robot-terrain in...
By Hojin Lee, Yunho Lee, Daniel A Duecker, Cheolhyeon Kwon
arXiv:2608. 06276v1 Announce Type: cross Abstract: Persistence diagrams (PDs) provide stable and interpretable summaries of multiscale topological structure.
By Farzana Nasrin
The paper presents a reward‑free continual learning framework for space robots that uses latent‑state world models to adapt to severe hardware degradation. By pre‑training a model‑based agent in diverse simulations, the world model learns to predict reward structure in latent space. During deployment, the observation encoder and reward predictor are frozen while only the transition dynamics are updated via unsupervised rollouts, allowing the policy to adapt using imagined trajectories without new rewards.
By Andrej Orsula, Miguel Olivares-Mendez, Carol Martinez
Spectral Prioritized Sweeping (SPS) extends traditional Prioritized Sweeping by incorporating graph topology through the resolvent and Laplacian diffusion, creating a smoother priority score that propagates reward changes more effectively in nonstationary reinforcement learning. The method, called Graph Topology Augmentation for Prioritized Sweeping (GTA-PS), uses a mixing of regularized Laplacian inverses and an adaptive scheduler based on the Second Largest Eigenvalue Modulus to adjust the influence of topology during replanning. Experiments on FourRooms and GARNET domains show that GTA-PS improves replanning efficiency compared to standard PS under both exact dynamic programming and Dyna-style planners.
By Hung Pham, Tuan Dam
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:2608. 14114v1 Announce Type: new Abstract: As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomous topological reconfiguration has emerged as a promising research field to keep strained grids stable and operational.
By Lukas Zetto, Benjamin Sch\"afer, Qiong Huang
arXiv:2607. 20656v1 Announce Type: cross Abstract: Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences.
By Manoosh Samiei, Doina Precup, Paul Masset
arXiv:2606. 00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions change.
By Purab Seth, Neil Shah, Kunal Jha, Samuel J. Gershman, Max Kleiman-Weiner, Wilka Carvalho