Safe Reinforcement Learning with Preference-based Constraint Inference
arXiv:2603.23565v1 Announce Type: new Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restrictive assumptions or extensive expert demonstrations, which is not realistic in many real-world applications. How to cheaply and reliably learn these constraints is the major challenge we focus on in this study. While inferring constraints from human preferences offers a data-efficient alternative, we identify the popular Bradley-Terry (BT) models fail to capture the asymmetric, heavy-tailed nature of safety costs, resulting in risk underestimation. It is still rare in the literature to understand the impacts of BT models on the downstream policy learning. To address the above knowledge gaps, we propose a novel approach namely Preference-based Constrained Reinforcement Learning (PbCRL). We in
arXiv:2603.23565v1 Announce Type: new Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restrictive assumptions or extensive expert demonstrations, which is not realistic in many real-world applications. How to cheaply and reliably learn these constraints is the major challenge we focus on in this study. While inferring constraints from human preferences offers a data-efficient alternative, we identify the popular Bradley-Terry (BT) models fail to capture the asymmetric, heavy-tailed nature of safety costs, resulting in risk underestimation. It is still rare in the literature to understand the impacts of BT models on the downstream policy learning. To address the above knowledge gaps, we propose a novel approach namely Preference-based Constrained Reinforcement Learning (PbCRL). We introduce a novel dead zone mechanism into preference modeling and theoretically prove that it encourages heavy-tailed cost distributions, thereby achieving better constraint alignment. Additionally, we incorporate a Signal-to-Noise Ratio (SNR) loss to encourage exploration by cost variances, which is found to benefit policy learning. Further, two-stage training strategy are deployed to lower online labeling burdens while adaptively enhancing constraint satisfaction. Empirical results demonstrate that PbCRL achieves superior alignment with true safety requirements and outperforms the state-of-the-art baselines in terms of safety and reward. Our work explores a promising and effective way for constraint inference in Safe RL, which has great potential in a range of safety-critical applications.
Executive Summary
This article presents a novel approach to safe reinforcement learning (RL) called Preference-based Constrained Reinforcement Learning (PbCRL). The authors address the challenge of inferring complex safety constraints from human preferences by introducing a dead zone mechanism and a Signal-to-Noise Ratio (SNR) loss. These innovations lead to better constraint alignment and improved policy learning. The authors' two-stage training strategy reduces online labeling burdens while enhancing constraint satisfaction. Empirical results demonstrate PbCRL's superiority over state-of-the-art baselines in terms of safety and reward. The work has significant implications for safety-critical applications and offers a promising solution for constraint inference in safe RL.
Key Points
- ▸ PbCRL introduces a dead zone mechanism to capture heavy-tailed cost distributions and encourage constraint alignment
- ▸ The approach incorporates a Signal-to-Noise Ratio (SNR) loss to promote exploration by cost variances
- ▸ A two-stage training strategy is deployed to lower online labeling burdens while enhancing constraint satisfaction
Merits
Strength in addressing the asymmetric and heavy-tailed nature of safety costs
The dead zone mechanism and SNR loss enable PbCRL to accurately capture these complexities and promote better policy learning.
Demerits
Potential overreliance on human preferences for constraint inference
While PbCRL offers a data-efficient alternative, human preferences may not always accurately reflect safety constraints, particularly in complex scenarios.
Expert Commentary
This article makes a substantial contribution to the field of safe RL by addressing a critical challenge in constraint inference. The authors' innovative approach, PbCRL, offers a promising solution for capturing the complexities of safety costs and promoting better policy learning. However, as with any approach relying on human preferences, there may be limitations in its generalizability and applicability. Further research is needed to explore the scalability and robustness of PbCRL in diverse safety-critical scenarios. Nevertheless, the work has significant implications for the development of safer and more reliable AI systems.
Recommendations
- ✓ Future research should investigate the scalability and robustness of PbCRL in diverse safety-critical scenarios
- ✓ The authors should explore alternative approaches to constraint inference that complement PbCRL and address potential limitations
Sources
Original: arXiv - cs.LG
