Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives

ArXi:2505.15693v3 Announce Type: replace Recent advances in reinforcement learning (RL) have renewed interest in reward design for shaping agent behavior, but manually crafting reward functions is tedious and error-prone. A principled alternative is to specify behavioral requirements in a formal, unambiguous language and automatically compile them into learning objectives. $\omega$-regular languages are a natural fit, given their role in formal verification and synthesis.