Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications

2026-02-20

AI Summary

Existing reward design methods struggle to capture fine-grained human preferences regarding agent behavior in long-horizon tasks, often resulting in task completion without adherence to desired behavioral norms. This work proposes a Hierarchical Reward Design framework with Language (HRDL) and its associated learning algorithm, L2HR, which for the first time integrates natural language–specified behavioral norms with hierarchical reinforcement learning. By parsing linguistic instructions, the framework automatically generates structured, multi-level reward functions that enable precise modeling of hierarchical behavioral preferences in complex tasks. Experimental results demonstrate that L2HR significantly outperforms existing approaches, effectively guiding agents to not only accomplish tasks but also conform to human-specified behavioral norms.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Intelligent Robots: Behavior Learning & Control

Application Category

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

Abstract

When training artificial intelligence (AI) to perform tasks, humans often care not only about whether a task is completed but also how it is performed. As AI agents tackle increasingly complex tasks, aligning their behavior with human-provided specifications becomes critical for responsible AI deployment. Reward design provides a direct channel for such alignment by translating human expectations into reward functions that guide reinforcement learning (RL). However, existing methods are often too limited to capture nuanced human preferences that arise in long-horizon tasks. Hence, we introduce Hierarchical Reward Design from Language (HRDL): a problem formulation that extends classical reward design to encode richer behavioral specifications for hierarchical RL agents. We further propose Language to Hierarchical Rewards (L2HR) as a solution to HRDL. Experiments show that AI agents trained with rewards designed via L2HR not only complete tasks effectively but also better adhere to human specifications. Together, HRDL and L2HR advance the research on human-aligned AI agents.

Problem

• reward design
• human alignment
• hierarchical reinforcement learning
• behavioral specifications
• language-based reward

Innovation

• Hierarchical Reward Design
• Language-to-Reward
• Human Alignment
• Reinforcement Learning
• Behavioral Specification