Adaptive Constraint Relaxation via Uncertainty Quantification in Safe RL

Safe RL often assumes constraints are rigid (see Liu et al., 2024, “Constraint Manifold”; Wei et al., 2023, “LSVI-AE”). But real-world systems sometimes tolerate “soft” boundaries, and being overly strict can stifle exploration and learning. Instead, what if constraints could adapt based on the agent’s epistemic uncertainty about the environment or its own policy? By integrating uncertainty estimates (e.g., via Bayesian neural networks or ensembles) into constraint satisfaction, the agent could relax constraints—temporarily and locally—when it’s confident, and tighten them when it detects ambiguity or risk. This idea challenges the core assumption of fixed constraints and builds on the meta-algorithmic uncertainty quantification in Wachi et al. (2023), but instead of just penalizing unsafe actions, it flexibly manages the exploration-safety tradeoff. This could enable safer, more sample-efficient learning in settings with partially known or evolving constraints.

References:

1. Safe Reinforcement Learning with Instantaneous Constraints: The Role of Aggressive Exploration. Honghao Wei, Xin Liu, Lei Ying (2023). AAAI Conference on Artificial Intelligence.
2. Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications. Puze Liu, Haitham Bou-Ammar, Jan Peters, Davide Tateo (2024). IEEE Transactions on robotics.
3. Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms. Akifumi Wachi, Wataru Hashimoto, Xun Shen, Kazumune Hashimoto (2023). Neural Information Processing Systems.