Reinforcement Learning With Adaptive Regularization For Safe Control Of Critical Systems

Abstract

Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Regularization (RL-AR), an algorithm that enables safe RL exploration by combining the RL policy with a policy regularizer that hard-codes the safety constraints. RL-AR performs policy combination via a "focus module," which determines the appropriate combination depending on the state--relying more on the safe policy regularizer for less-exploited states while allowing unbiased convergence for well-exploited states. In a series of critical control applications, we demonstrate that RL-AR not only ensures safety during training but also achieves a return competitive with the standards of model-free RL that disregards safety.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keytian2024reinforcement

Related papers

• Safe Continual Reinforcement Learning In Non-stationary Environments (2026)12.89
• Actsafe: Active Exploration With Safety Constraints For Reinforcement Learning (2024)0.00
• Safe Reinforcement Learning With Dual Robustness (2023)8.60
• Concurrent Learning Of Policy And Unknown Safety Constraints In Reinforcement Learning (2024)0.00
• Regularization Matters In Policy Optimization (2019)2.68
• On The Robustness Of Safe Reinforcement Learning Under Observational Perturbations (2022)0.00
• Provably Optimal Reinforcement Learning Under Safety Filtering (2025)0.00
• Safety Correction From Baseline: Towards The Risk-aware Policy In Robotics Via Dual-agent Reinforcement Learning (2022)3.58