arXiv:2608. 16666v1 Announce Type: new Abstract: This paper presents Chronocooked, a reinforcement learning (RL) benchmark suite for studying implicit interval timing in RL agents.
By Amrapali Pednekar, Alvaro Garrido-Perez, Yara Khaluf, Pieter Simoens
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:2608. 11511v1 Announce Type: cross Abstract: In sequential decision making, an agent typically observes its environment and acts at every timestep.
By Christopher Watson, Arjun Krishna, Dinesh Jayaraman, Rajeev Alur
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:2605. 11484v2 Announce Type: replace Abstract: Task completion in digital and physical environments increasingly involves complex temporal interaction, where actions and observations unfold over different time scales rather than align with fixed observation--action steps.
By Jialian Li, Yuchen Cao, Junhong Liu, Weiran Guo, Xutao Wang, Jiaming Song, Jiahao Zhang, Jie Chen
arXiv:2609.05519v1 Announce Type: cross
Abstract: We propose a unified strategy for fast goal inference in human-robot interaction. The core idea is to drive the human toward Critical Decision Points...
By Debasmita Ghose, Oz Gitelson, Michal Lewkowicz, Jake Brawer, Marynel Vazquez, Brian Scassellati