Learning to Wait: A New Reinforcement Learning Approach for Sequential Decision Making
A new research paper on arXiv (2608.11511) introduces a method for training agents to 'wait' in sequential decision-making tasks, potentially reducing resource consumption without sacrificing performance. The approach, which uses reinforcement learning with lexicographic optimization, allows agents to decide when and how long to pause sensing and acting, letting the environment evolve on its own. This is particularly useful for tasks like brewing coffee, where constant monitoring is unnecessary. The paper formalizes 'learning to wait' as minimizing sensing frequency while maintaining task performance, such as total completion time. The research was announced as a cross-type submission and is available at the provided arXiv URL.
Key facts
- The paper is titled 'Let it Cook: Learning to Wait in Sequential Decision Making'.
- It is available on arXiv with identifier 2608.11511.
- The research proposes a 'waiting policy' that determines where and how long to wait.
- The approach uses reinforcement learning with lexicographic optimization.
- Waiting involves forgoing sensing for a set number of timesteps.
- The goal is to minimize sensing frequency without sacrificing task performance.
- Example application: brewing coffee, where constant monitoring is not needed.
- The paper was announced with an 'Announce Type: cross'.
Entities
Institutions
- arXiv