# Let it Cook: Learning to Wait in Sequential Decision Making

arXiv: https://arxiv.org/abs/2608.11511 · HTML: https://mlsanity.com/paper/2608.11511/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 11 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Octo: An Open-Source Generalist Robot Policy](https://mlsanity.com/paper/2405.12213/) (2024)
- [GR00T N1: An Open Foundation Model for Generalist Humanoid Robots](https://mlsanity.com/paper/2503.14734/) (2025)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks](https://mlsanity.com/paper/2306.13831/) (2023)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [When Waiting is not an Option : Learning Options with a Deliberation Cost](https://mlsanity.com/paper/1709.04571/) (2017)
- [skrl: Modular and Flexible Library for Reinforcement Learning](https://mlsanity.com/paper/2202.03825/) (2022)
- [Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning](https://mlsanity.com/paper/1702.06054/) (2017)
- [TempoRL: Learning When to Act](https://mlsanity.com/paper/2106.05262/) (2021)
- [Hierarchical Reinforcement Learning with Timed Subgoals](https://mlsanity.com/paper/2112.03100/) (2021)
- [Lexicographic Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2212.13769/) (2022)
- [Maximum Hands-Off Control: A Paradigm of Control Effort Minimization](https://mlsanity.com/paper/1408.3025/) (2014)

## Built on by (most cited first, 1 of 1)

- [Chronocooked: A Benchmark for Interval Timing in Reinforcement Learning Agents](https://mlsanity.com/paper/2608.16666/) (2026)

## Abstract (arXiv)

In sequential decision making, an agent typically observes its environment and acts at every timestep. However, such active participation may not always be necessary; tasks such as brewing coffee include periods that are served equally well by letting the environment evolve without constant monitoring and control. During such periods, the agent could simply wait to conserve its resources, or redirect its attention to another task. We capitalize on these opportunities by training a "waiting policy" that decides where and how long to wait. This involves forgoing sensing to commit to a wait action, representing a deliberate pause for a set number of timesteps. We formalize "learning to wait" as minimizing the frequency of sensing and decision making without sacrificing task performance (e.g., the total amount of time to complete a task). To train a waiting policy, we propose an approach that employs reinforcement learning with lexicographically ordered objectives. In experiments across 4 discrete-state household tasks and 3 continuous-state environments, we show that our approach successfully learns waiting behaviors, and can adapt pre-trained policies to wait where appropriate. While different tasks permit different amounts of waiting without sacrificing task performance, our approach consistently finds solutions with significant waiting, sometimes waiting for over 50 percent of the task duration.