Directional Constraints for Efficient Exploration in Safe Reinforcement Learning

2026-07-14

AI Summary

This work addresses the trade-off between safety and performance in safe reinforcement learning, where hard constraints often impair exploration efficiency and task performance. The authors propose ATACOM-DC, an extension of the ATACOM safety layer that introduces a directional constraint mechanism. This mechanism activates safety interventions only when the agent moves toward constraint boundaries, distinguishing between actions that approach versus recede from violations to enforce constraints on demand. ATACOM-DC flexibly incorporates either prior knowledge or data-driven models for constraint representation and integrates seamlessly into existing reinforcement learning algorithms. Evaluated across multiple simulated robotic control tasks, the method significantly reduces constraint violation costs while simultaneously improving task performance, thereby achieving a more favorable balance between safety and efficiency.

Abstract

Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in open-ended environments requires strong safety guarantees to prevent dangerous or harmful behaviors. Safe Reinforcement Learning methods address this requirement by enforcing safety constraints. Nevertheless, learning under constraints often reduces learning speed and could lead to suboptimal task performance, as the agent must solve a more complex constrained optimization problem compared to unconstrained settings. To tackle this issue, in this work, we propose an extension of the ATACOM framework, a state-of-the-art reliable safety layer that can be integrated with existing Reinforcement Learning algorithms to enforce constraints derived from prior knowledge of the system or learned directly from data. Our proposed method, named ATACOM Directional Constraints (ATACOM-DC), significantly improves the safety-performance trade-off by introducing directional constraints that distinguish between actions approaching and moving away from constraint boundaries, activating constraint enforcement only when necessary. We evaluate our method across a range of challenging robotic control tasks in simulation, analyzing both constraint-violation costs and achieved task performance. Code and additional material at https://atacom-dc.robot-learning.net.

Problem

• Safe Reinforcement Learning
• Safety Constraints
• Exploration Efficiency
• Performance-Safety Trade-off
• Robotic Control

Innovation

• Directional Constraints
• Safe Reinforcement Learning
• ATACOM
• Constraint Enforcement
• Safety-Performance Trade-off