# A Framework for Constrained and Adaptive Behavior-Based Agents

arXiv: https://arxiv.org/abs/1506.02312 · HTML: https://mlsanity.com/paper/1506.02312/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2015.

## Built on by (most cited first, 8 of 8)

- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions](https://mlsanity.com/paper/2405.07474/) (2024)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration](https://mlsanity.com/paper/2502.18072/) (2025)
- [Behavior Trees Enable Structured Programming of Language Model Agents](https://mlsanity.com/paper/2404.07439/) (2024)
- [Combining Reinforcement Learning and Behavior Trees for NPCs in Video Games with AMD Schola](https://mlsanity.com/paper/2510.14154/) (2025)
- [Progress Constraints for Reinforcement Learning in Behavior Trees](https://mlsanity.com/paper/2602.06525/) (2026)
- [CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation](https://mlsanity.com/paper/2603.16809/) (2026)

## Abstract (arXiv)

Behavior Trees are commonly used to model agents for robotics and games, where constrained behaviors must be designed by human experts in order to guarantee that these agents will execute a specific chain of actions given a specific set of perceptions. In such application areas, learning is a desirable feature to provide agents with the ability to adapt and improve interactions with humans and environment, but often discarded due to its unreliability. In this paper, we propose a framework that uses Reinforcement Learning nodes as part of Behavior Trees to address the problem of adding learning capabilities in constrained agents. We show how this framework relates to Options in Hierarchical Reinforcement Learning, ensuring convergence of nested learning nodes, and we empirically show that the learning nodes do not affect the execution of other nodes in the tree.