Integrating LTL Constraints into PPO for Safe Reinforcement Learning

Researchers developed PPO-LTL, a new framework that integrates Linear Temporal Logic safety constraints into Proximal Policy Optimization for safer reinforcement learning. The system uses Büchi automata to monitor safety violations and converts them into penalty signals, showing reduced safety violations while maintaining competitive performance in robotics environments.

• →PPO-LTL framework integrates rigorous safety constraints written in Linear Temporal Logic into reinforcement learning algorithms.
• →The system uses limit-deterministic Büchi automata to monitor violations and translate them into penalty signals for policy optimization.
• →Experiments on Zones and CARLA environments demonstrate consistent reduction in safety violations compared to state-of-the-art methods.
• →The framework maintains competitive performance while improving safety, making it suitable for robotics applications with regulatory requirements.
• →Open-source implementation is available on GitHub for research and development use.