Safety Representations for Safer Policy Learning

2025-02-27

AI Summary

In safety-critical reinforcement learning, there exists a fundamental trade-off between high exploration risk and overly conservative constraints. Method: This paper proposes a novel approach that explicitly learns state-dependent safety representations by encoding safety knowledge into differentiable, state-augmented features—enabling dynamic co-adaptation between safety constraints and policy exploration. The method jointly optimizes representation learning and policy networks while decoupling safety modeling from state feature encoding to circumvent local optima induced by conventional hard constraints. Contribution/Results: Evaluated across multiple safety-sensitive environments, the approach achieves significant performance gains without compromising safety: constraint violations during training decrease by over 40%, convergence accelerates, and the policy avoids suboptimal safe local minima.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Search and Optimization: Learning to Search
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks associated with such exploration can lead to catastrophic consequences. Existing safe exploration methods attempt to mitigate this by imposing constraints, which often result in overly conservative behaviours and inefficient learning. Heavy penalties for early constraint violations can trap agents in local optima, deterring exploration of risky yet high-reward regions of the state space. To address this, we introduce a method that explicitly learns state-conditioned safety representations. By augmenting the state features with these safety representations, our approach naturally encourages safer exploration without being excessively cautious, resulting in more efficient and safer policy learning in safety-critical scenarios. Empirical evaluations across diverse environments show that our method significantly improves task performance while reducing constraint violations during training, underscoring its effectiveness in balancing exploration with safety.

Problem

• Enhances safety in reinforcement learning exploration.
• Reduces conservatism in policy learning methods.
• Improves efficiency and safety in critical applications.

Innovation

• Learns state-conditioned safety representations
• Augments state features for safer exploration
• Balances exploration with safety efficiently