PlatoLTL: Scaling LTL-Guided Multi-Task RL

Linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks in multi-task reinforcement learning (RL). However, while existing approaches in LTL-guided multi-task RL demonstrate success in simple environments, they suffer from challenges in *representation learning* and *exploration efficiency* when applied to high-dimensional environments with *parameterized* specifications. We present PlatoLTL, which elevates state-of-the-art methods to address both challenges. We model atomic propositions as instances of *atomic predicates* and inject task parameters directly into the goal embedding to enable efficient generalization. We also leverage *simple priors* on closeness to predicate satisfaction to enable and accelerate learning of complex tasks without biasing the optimal policy. We validate our approach on challenging environments including robotic manipulation and multi-drone navigation.