When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
Quick Answer
This paper explores meta-reasoning in AI, introducing a reinforcement learning method that allows agents to determine when to switch from reactive decision-making to deliberative planning based on uncertainty scores.
Quick Take
The empirical study demonstrates that this approach enhances decision-making in motion planning and navigation tasks, allowing agents to adaptively choose between fast actions and more computationally intensive planning as needed.
Key Points
- Introduces a meta-reasoning policy using reinforcement learning for AI agents.
- Agents use a reactive-policy uncertainty score to decide on planning needs.
- Empirical results show improved decision-making in motion planning tasks.
- The design allows agents to shift towards fully reactive control as policies improve.
- Addresses the trade-off between speed and accuracy in AI decision-making.
DeepSignal Analysis
What happened
The paper investigates meta-reasoning in AI, focusing on how agents can learn to switch between reactive decision-making and deliberative planning. It introduces a reinforcement learning method that utilizes uncertainty scores to determine when to plan versus act. The empirical study shows improved decision-making in motion planning and navigation tasks.
Key evidence
- The study models reactive decision-making as a policy mapping state observations to actions, which can be trained using reinforcement learning or imitation learning.
- The proposed method allows agents to predict when the reactive policy may fail, indicating when deliberative planning is necessary.
- Empirical results demonstrate that the meta-reasoning policy effectively learns to balance between fast actions and computationally intensive planning.
Why it matters
Understanding how to effectively switch between reactive and deliberative strategies is crucial for enhancing AI decision-making capabilities. This research could lead to more adaptable AI systems that perform better in dynamic environments, improving applications in robotics and autonomous navigation.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning. In this paper, we study the question of how to learn this ability, known as meta-reasoning, in artificial agents. We model reactive decision-making as a policy that directly maps state observations to actions. Such policies can be trained with reinforcement learning (RL) or imitation learning, but may generalize poorly outside of their training distribution. Alternatively, model-based decision-time planning is more likely to produce good actions across a broader set of states but requires additional computation time, which delays acting. In this work, we introduce an RL method for training a meta-reasoning policy that allocates computation by conditioning on a reactive-policy uncertainty score. This score enables it to predict when the reactive policy is likely to perform poorly and when planning is needed. We conduct an empirical study on motion planning and navigation environments, showing that this design enables the meta-reasoning policy to learn when the reactive policy provides a good-enough action versus when decision-time planning is needed. Additionally, we show that our design enables the meta-agent to shift toward fully reactive control as the reactive policy improves.
| Comments: | RLC 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.16421 [cs.AI] |
| (or arXiv:2607.16421v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.16421 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Adam Labiosa [view email]
[v1]
Fri, 17 Jul 2026 18:13:38 UTC (1,372 KB)
— Originally published at arxiv.org
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