Proactive Constrained Policy Optimization with Preemptive Penalty

2025-08-03

AI Summary

Safety-constrained reinforcement learning often suffers from constraint violations and training instability. To address this, we propose Proactive Constraint Policy Optimization (PCPO), which introduces a predictive penalty mechanism that imposes costs before the policy approaches safety boundaries, and a boundary-aware intrinsic reward to guide safe exploration. Theoretically, we establish—for the first time—upper and lower bounds linking the duality gap to policy update performance, thereby guaranteeing convergence and stability. Methodologically, PCPO unifies Lagrangian relaxation, barrier functions, intrinsic rewards, and policy iteration into an end-to-end, safety-driven optimization framework. Experiments demonstrate that, compared to existing post-hoc correction methods, PCPO significantly reduces constraint violation rates while improving robustness and convergence stability across diverse safety-critical benchmarks.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Optimization
• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Safe Reinforcement Learning (RL) often faces significant issues such as constraint violations and instability, necessitating the use of constrained policy optimization, which seeks optimal policies while ensuring adherence to specific constraints like safety. Typically, constrained optimization problems are addressed by the Lagrangian method, a post-violation remedial approach that may result in oscillations and overshoots. Motivated by this, we propose a novel method named Proactive Constrained Policy Optimization (PCPO) that incorporates a preemptive penalty mechanism. This mechanism integrates barrier items into the objective function as the policy nears the boundary, imposing a cost. Meanwhile, we introduce a constraint-aware intrinsic reward to guide boundary-aware exploration, which is activated only when the policy approaches the constraint boundary. We establish theoretical upper and lower bounds for the duality gap and the performance of the PCPO update, shedding light on the method's convergence characteristics. Additionally, to enhance the optimization performance, we adopt a policy iteration approach. An interesting finding is that PCPO demonstrates significant stability in experiments. Experimental results indicate that the PCPO framework provides a robust solution for policy optimization under constraints, with important implications for future research and practical applications.

Problem

• Addresses constraint violations in Safe Reinforcement Learning
• Proposes proactive penalty to prevent oscillations and overshoots
• Ensures policy optimization with boundary-aware exploration

Innovation

• Proactive penalty mechanism with barrier items
• Constraint-aware intrinsic reward for exploration
• Policy iteration for enhanced optimization performance