Researchers propose extending Reward Machines with Signal Temporal Logic (STL) formulas to improve reinforcement learning for complex control tasks. STL enables more efficient reward representation and guides training toward specified behavioral requirements. The framework is validated on minigrid, cart-pole, and highway environments.
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On Tackling Complex Tasks with Reward Machines and Signal Temporal Logics
Researchers boost reinforcement learning efficiency by replacing traditional rewards with Signal Temporal Logic formulas, enabling clearer formal specifications for complex control tasks.
Friday, April 17, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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