Reward Generation for Model-Free Reinforcement Learning from Formal Specifications
This work proposes a new quantitative semantics for STL having several desirable properties, making it suitable for reward generation, and establishes the new semantics to be the most suitable for synthesizing feedback controllers for complex continuous dynamical systems through reinforcement learning.