Academic

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

arXiv:2602.18582v1 Announce Type: new 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

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Zhiqin Qian, Ryan Diaz, Sangwon Seo, Vaibhav Unhelkar · · 1 min read · 2 views
#cs.AI #cs.CL #cs.HC #cs.LG

arXiv:2602.18582v1 Announce Type: new 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.

Executive Summary

This study proposes Hierarchical Reward Design from Language (HRDL) to enhance the alignment of artificial intelligence (AI) agent behavior with human specifications. HRDL is a problem formulation that extends classical reward design to encode richer behavioral specifications for hierarchical reinforcement learning (RL) agents. Language to Hierarchical Rewards (L2HR) is introduced as a solution to HRDL. Experiments demonstrate improved task completion and adherence to human specifications when AI agents are trained with rewards designed via L2HR. This research contributes to the development of human-aligned AI agents, critical for responsible AI deployment. The approach has the potential to address the limitations of existing reward design methods and facilitate more nuanced human-AI collaboration.

Key Points

  • ▸ HRDL extends classical reward design to encode richer behavioral specifications for hierarchical RL agents.
  • ▸ L2HR is proposed as a solution to HRDL, enabling the design of rewards from language-based specifications.
  • ▸ Experiments show improved task completion and adherence to human specifications with L2HR-designed rewards.

Merits

Strength in Addressing Long-Horizon Tasks

HRDL and L2HR are well-suited to address the complexities of long-horizon tasks, where existing methods often falter.

Improved Human-AI Alignment

The proposed approach enables the design of rewards that more accurately capture nuanced human preferences and specifications.

Enhanced Responsible AI Deployment

HRDL and L2HR contribute to the development of human-aligned AI agents, critical for responsible AI deployment and adoption.

Demerits

Limited Experimental Scope

The study's experimental focus on a specific task and domain may limit the generalizability of the findings to other areas.

Technical Complexity

The introduction of HRDL and L2HR may add to the technical complexity of reward design, requiring significant expertise to implement.

Scalability

The scalability of HRDL and L2HR to larger, more complex tasks remains an open question.

Expert Commentary

The introduction of HRDL and L2HR represents a significant advancement in the field of human-AI alignment and reward design. While the study's experimental scope is limited, the approach has the potential to address the complexities of long-horizon tasks and facilitate more nuanced human-AI collaboration. However, the technical complexity and scalability of HRDL and L2HR remain open questions that require further investigation.

Recommendations

  • ✓ Further research is needed to explore the scalability and generalizability of HRDL and L2HR to larger, more complex tasks.
  • ✓ The development of tools and frameworks to support the implementation of HRDL and L2HR could facilitate broader adoption and application of the approach.

Sources

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