This paper presents Chronocooked, a reinforcement learning (RL) benchmark suite for studying implicit interval timing in RL agents. Inspired by Overcooked, the suite comprises cooking scenarios that require temporal decision making.
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.
arXiv:2607. 20656v1 Announce Type: cross Abstract: Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences.
arXiv:2608. 11511v1 Announce Type: cross Abstract: In sequential decision making, an agent typically observes its environment and acts at every timestep.
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.
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.
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...
The paper introduces Temporal-Logic-based Causal Diagrams (TL-CDs) for reinforcement learning tasks that involve temporally extended goals. TL-CDs encode causal relationships among environmental properties, complementing deterministic finite automata that model rewards. By leveraging TL-CDs, the authors design an RL algorithm that can predict expected rewards early, leading to significantly reduced exploration and faster convergence to optimal policies.
Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and penalty-based approaches seek spatial smoothness by...
The paper investigates whether small large language model agents can operate within explicit wall‑clock time budgets while using the allotted time productively. Experiments with Qwen3.6‑27B on MLE‑Bench Lite and Qwen3‑4B on Zork I show that agents fail to translate budget statements into controlled time use, largely due to lack of timing feedback and poor anticipation of action durations. Interventions such as harness‑based timing exposure, enforcement hooks, and reinforcement learning with budget‑aware rewards improve budget adherence but do not lead to better task performance, revealing a persistent gap between respecting time limits and effectively allocating extra time.
EgoSpeedUp is a framework that transfers human manipulation tempo to robot policies by aligning and retiming robot demonstrations using phase-wise tempo estimates derived from human demonstrations. The method improves task success rates by an average of 25 percentage points and reduces successful execution time by 36.5% on two real-world manipulation tasks. It demonstrates that human manipulation tempo can serve as an effective temporal reference for faster and more reliable robot policies.
arXiv:2608. 13625v1 Announce Type: new Abstract: Signal temporal logic (STL) provides a formal language for specifying real-time properties of real-valued observations, along with a quantitative robustness score for monitoring satisfaction.
The paper introduces Q-Shaped Options (QSO), a hierarchical reinforcement learning framework that learns action abstractions between consecutive levels using shared encoders shaped by Q functions. QSO employs distinct state-value functions, Q functions, and policies at each hierarchy level, encouraging the low-level Q function to preserve distinctions needed for optimal control while the high-level Q function discards unnecessary distinctions. Experiments on offline goal-conditioned locomotion and manipulation tasks show that QSO learns semantically meaningful option spaces and outperforms baselines, achieving non-zero performance where other algorithms fail.