Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications

Understand

This paper explores continuous-time control synthesis for target-driven navigation to satisfy complex high-level tasks expressed as linear temporal logic (LTL).

• We propose a model-free framework using deep reinforcement learning (DRL) where the underlying dynamic system is unknown (an opaque box).
• Unlike prior work, this paper considers scenarios where the given LTL specification might be infeasible and therefore cannot be accomplished globally.
• Instead of modifying the given LTL formula, we provide a general DRL-based approach to satisfy it with minimal violation.