Neuro-Symbolic Injection of LTLf Constraints in Autoregressive Reinforcement Learning Policies
arXiv:2606.08312v1 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. Here, we introduce a neurosymbolic framework that injects LTLf background knowledge into such transformer-based RL polici
The increased sophistication of transformer models in reinforcement learning creates a timely need for mechanisms to inject complex temporal constraints, moving beyond simple reward optimization.
This development allows AI systems to incorporate high-level, human-understandable temporal logic into their decision-making, enabling more reliable and alignable autonomous agents.
Reinforcement learning policies can now be systematically guided by formal temporal constraints, enhancing safety, predictability, and compliance in complex autonomous systems.
- · AI agents developers
- · Robotics industry
- · Safety-critical AI applications
- · Formal verification tooling providers
- · Purely reward-driven RL approaches
- · Teams unable to integrate neuro-symbolic methods
AI agents will exhibit improved adherence to complex operational procedures and safety protocols.
Reduced need for extensive manual oversight in certain autonomous systems as their high-level behavior becomes more predictable.
Accelerated development and adoption of AI in highly regulated and safety-conscious industries, potentially impacting labor in those sectors.
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Read at arXiv cs.AI