Progress Constraints for Reinforcement Learning in Behavior Trees

2026-02-06

AI Summary

This work addresses the performance degradation that arises when integrating behavior trees with reinforcement learning, where conflicting actions from different controllers can disrupt already achieved subgoals. To mitigate this issue, the authors propose a “progress constraint” mechanism that leverages convergence theory of behavior trees to construct a feasibility estimator, which dynamically restricts the action space of the reinforcement learning agent. This ensures continuous task progress and prevents subgoal interference while preserving the structured decision-making benefits of behavior trees. Experimental results in both a 2D proof-of-concept environment and a high-fidelity warehouse simulation demonstrate that the proposed approach significantly outperforms existing methods, achieving notable improvements in task performance, sample efficiency, and constraint satisfaction rate.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Behavior Learning & Control
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Behavior Trees (BTs) provide a structured and reactive framework for decision-making, commonly used to switch between sub-controllers based on environmental conditions. Reinforcement Learning (RL), on the other hand, can learn near-optimal controllers but sometimes struggles with sparse rewards, safe exploration, and long-horizon credit assignment. Combining BTs with RL has the potential for mutual benefit: a BT design encodes structured domain knowledge that can simplify RL training, while RL enables automatic learning of the controllers within BTs. However, naive integration of BTs and RL can lead to some controllers counteracting other controllers, possibly undoing previously achieved subgoals, thereby degrading the overall performance. To address this, we propose progress constraints, a novel mechanism where feasibility estimators constrain the allowed action set based on theoretical BT convergence results. Empirical evaluations in a 2D proof-of-concept and a high-fidelity warehouse environment demonstrate improved performance, sample efficiency, and constraint satisfaction, compared to prior methods of BT-RL integration.

Problem

• Behavior Trees
• Reinforcement Learning
• progress constraints
• controller interference
• subgoal preservation

Innovation

• Progress Constraints
• Behavior Trees
• Reinforcement Learning
• Feasibility Estimators
• Constrained Action Sets