Safe Reinforcement Learning via Probabilistic Logic Shields
Safe Reinforcement Learning via Probabilistic Logic Shields
Wen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 5739-5749.
https://doi.org/10.24963/ijcai.2023/637
Safe Reinforcement learning (Safe RL) aims at learning optimal policies while staying safe. A popular solution to Safe RL is shielding, which uses a logical safety specification to prevent an RL agent from taking unsafe actions. However, traditional shielding techniques are difficult to integrate with continuous, end-to-end deep RL methods. To this end, we introduce Probabilistic Logic Policy Gradient (PLPG). PLPG is a model-based Safe RL technique that uses probabilistic logic programming to model logical safety constraints as differentiable functions. Therefore, PLPG can be seamlessly applied to any policy gradient algorithm while still providing the same convergence guarantees. In our experiments, we show that PLPG learns safer and more rewarding policies compared to other state-of-the-art shielding techniques.
Keywords:
Uncertainty in AI: UAI: Statistical relational AI
Knowledge Representation and Reasoning: KRR: Learning and reasoning
Machine Learning: ML: Reinforcement learning