Neuro-Symbolic Injection of LTLf Constraints

Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies

Announce Type: new Abstract: In this work we study offline reinforcement learning (RL) under temporally extended task constraints expressed in Linear Temporal Logic over finite traces (LTLf). Recently, transformer-based approaches such as Trajectory Transformers and Decision Transformers have been adopted to address RL as a sequence modeling problem. However, these methods optimize purely for reward and do not account for high-level temporal requirements.