Reinforcement Learning for Signal Temporal Logic using Funnel-Based Approach
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Signal Temporal Logic (STL) is a powerful framework for describing the complex temporal and logical behaviour of the dynamical system. Several works propose a method to find a controller for the satisfaction of STL specification using reinforcement learning but fail to address either the issue of robust satisfaction in continuous state space or ensure the tractability of the approach. In this paper, leveraging the concept of funnel functions, we propose a tractable reinforcement learning algorithm to learn a time-dependent policy for robust satisfaction of STL specification in continuous state space. We demonstrate the utility of our approach on several tasks using a pendulum and mobile robot examples.
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