<ul><li>A new learning approach has been proposed to efficiently satisfy complex Linear Temporal Logic (LTL) specifications in multi-task reinforcement learning (RL).</li><li>Existing approaches for satisfying LTL specifications suffer from various limitations, such as only being applicable to finite-horizon fragments of LTL, suboptimal solutions, and insufficient handling of safety constraints.</li><li>The proposed method uses B"uchi automata to represent the semantics of LTL specifications and learns policies based on sequences of truth assignments.</li><li>Experiments show that the approach can zero-shot satisfy a wide range of specifications, both finite- and infinite-horizon, and outperforms existing methods in terms of satisfaction probability and efficiency.</li></ul>

DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL

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