Reward-Petri-Net Interpretation of Temporal Behavior Trees

This paper presents a Reward-Petri-Net interpretation of Temporal Behavior Trees for reinforcement learning. It translates TBTs into Petri Nets, assigning rewards based on structural constraints defined in Linear Temporal Logic, enabling effective learning in complex, long-horizon robotic tasks where vanilla RL fails.