The paper introduces SUN, a reachability-aware goal-selection framework for reinforcement learning that jointly considers novelty and reachability. SUN uses successor value functions to identify goals that are both novel and reachable, proving properties such as recovering count-based bonuses, bounding short-horizon hitting probabilities, and rejecting unreachable goals. An adaptive goal-selection strategy and a lightweight pseudocount are proposed, and extensive benchmarks show SUN outperforming state-of-the-art methods across diverse environments.
The paper introduces robust successor features, a method that extends the successor representation to handle uncertainty in both reward functions and transition kernels within linear Markov Decision Processes. It provides a theoretical bound on Generalized Policy Improvement that quantifies performance loss due to mismatched dynamics, and demonstrates the approach on grid-based benchmarks against prior methods that consider only reward or transition differences.
By Erik Nikulski, Yamen Habib, Vicen\c{c} Gomez, Anders Jonsson, Rub\'en Moreno-Bote, Javier Segovia-Aguas
arXiv:2606. 00151v1 Announce Type: cross Abstract: In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can improve performance or reduce uncertainty; without such retries, a greedy policy is optimal.
By Soichiro Nishimori, Paavo Parmas, Sotetsu Koyamada, Tadashi Kozuno, Toshinori Kitamura, Shin Ishii, Yutaka Matsuo
The paper introduces Generalized Implicit Temporal Abstraction (GITA), a method for goal-conditioned reinforcement learning that conditions a single value function on multiple temporal abstraction levels (k). By aggregating advantage-weighted supervision across various k values, GITA preserves both long-range signal and local resolution without committing to a single k. Experiments on OGBench show that GITA outperforms existing offline GCRL baselines, improving average success rates by 25 percentage points over HIQL and 7 percentage points over OTA.
By Pedro Robles Dutenhefner, Dikshant Shehmar, Wagner Meira Jr., Marlos C. Machado
arXiv:2507. 15356v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) learns policies from fixed datasets, thereby avoiding costly or unsafe environment interactions.
By Lu Guo, Yixiang Shan, Zhengbang Zhu, Qifan Liang, Lichang Song, Ting Long, Weinan Zhang, Yi Chang
arXiv:2606. 24994v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) for language-model reasoning can fail at both extremes of task difficulty: easy prompts often produce all-correct, low-diversity rollout groups with little gradient signal, while hard prompts can produce all-incorrect groups with no positive reward.
By Wenyang Hu, Junxiang Jia, Zhen Shu, Daniel Dahlmeier, See-Kiong Ng, Bryan Kian Hsiang Low