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Signal temporal logic (STL) provides a user-friendly interface for definingcomplex tasks for robotic systems. Recent efforts aim at designing control lawsor using reinforcement learning methods to find policies which guaranteesatisfaction of these tasks. While the former suffer from the trade-off betweentask specification and computational complexity, the latter encounterdifficulties in exploration as the tasks become more complex and challenging tosatisfy. This paper proposes to combine the benefits of the two approaches anduse an efficient prescribed performance control (PPC) base law to guideexploration within the reinforcement learning algorithm. The potential of themethod is demonstrated in a simulated environment through two samplenavigational tasks.

Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks