Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback
arXiv:2602.20728v1 Announce Type: new Abstract: Reward design has been one of the central challenges for real world reinforcement learning (RL) deployment, especially in settings with multiple objectives. Preference-based RL offers an appealing alternative by learning from human preferences over pairs of behavioural outcomes. More recently, RL from AI feedback (RLAIF) has demonstrated that large language models (LLMs) can generate preference labels at scale, mitigating the reliance on human annotators. However, existing RLAIF work typically focuses only on single-objective tasks, leaving the open question of how RLAIF handles systems that involve multiple objectives. In such systems trade-offs among conflicting objectives are difficult to specify, and policies risk collapsing into optimizing for a dominant goal. In this paper, we explore the extension of the RLAIF paradigm to multi-objective self-adaptive systems. We show that multi-objective RLAIF can produce policies that yield bala
arXiv:2602.20728v1 Announce Type: new Abstract: Reward design has been one of the central challenges for real world reinforcement learning (RL) deployment, especially in settings with multiple objectives. Preference-based RL offers an appealing alternative by learning from human preferences over pairs of behavioural outcomes. More recently, RL from AI feedback (RLAIF) has demonstrated that large language models (LLMs) can generate preference labels at scale, mitigating the reliance on human annotators. However, existing RLAIF work typically focuses only on single-objective tasks, leaving the open question of how RLAIF handles systems that involve multiple objectives. In such systems trade-offs among conflicting objectives are difficult to specify, and policies risk collapsing into optimizing for a dominant goal. In this paper, we explore the extension of the RLAIF paradigm to multi-objective self-adaptive systems. We show that multi-objective RLAIF can produce policies that yield balanced trade-offs reflecting different user priorities without laborious reward engineering. We argue that integrating RLAIF into multi-objective RL offers a scalable path toward user-aligned policy learning in domains with inherently conflicting objectives.
Executive Summary
This article explores the extension of Reinforcement Learning from AI Feedback (RLAIF) to multi-objective systems. The authors demonstrate that multi-objective RLAIF can produce policies that balance conflicting objectives by learning from human preferences over pairs of behavioral outcomes. This approach mitigates the need for laborious reward engineering and offers a scalable path toward user-aligned policy learning in domains with inherent conflicts. The authors' findings have significant implications for the development of intelligent transportation systems, where balancing multiple objectives is crucial. By leveraging large language models to generate preference labels, the authors provide a promising solution to the challenge of reward design in real-world reinforcement learning deployment.
Key Points
- ▸ The authors extend the RLAIF paradigm to multi-objective self-adaptive systems.
- ▸ Multi-objective RLAIF produces policies that balance conflicting objectives without laborious reward engineering.
- ▸ The approach leverages large language models to generate preference labels at scale.
Merits
Strength
The authors' approach addresses a significant challenge in reinforcement learning, namely the difficulty of designing rewards for systems with multiple objectives. By leveraging AI feedback, the authors provide a scalable solution to this challenge.
Strength
The authors demonstrate the effectiveness of their approach in a real-world application, specifically in urban traffic control.
Demerits
Limitation
The authors' approach relies on human preferences to generate preference labels, which may be subject to bias and variability.
Limitation
The authors do not provide a comprehensive evaluation of the performance of their approach compared to other reinforcement learning methods.
Expert Commentary
The article makes a significant contribution to the field of reinforcement learning by extending the RLAIF paradigm to multi-objective systems. The authors' use of large language models to generate preference labels at scale is a promising solution to the challenge of reward design. However, the approach relies on human preferences, which may be subject to bias and variability. A comprehensive evaluation of the performance of the approach compared to other reinforcement learning methods is necessary to fully understand its strengths and limitations. The implications of the authors' findings are significant, particularly for the development of intelligent transportation systems and user-centered AI design.
Recommendations
- ✓ Future research should focus on developing methods to mitigate the bias and variability associated with human preferences in the RLAIF paradigm.
- ✓ A comprehensive evaluation of the performance of the RLAIF paradigm compared to other reinforcement learning methods is necessary to fully understand its strengths and limitations.