A policy that takes both an LTL instruction and a mapping specification as inputs can satisfy symbols under varied criteria, outperforming context-aware multi-task RL baselines in navigation and inspection simulations.
The temporal logic of programs,
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Reinforcement Learning of Flexible Policies for Symbolic Instructions with Adjustable Mapping Specifications
A policy that takes both an LTL instruction and a mapping specification as inputs can satisfy symbols under varied criteria, outperforming context-aware multi-task RL baselines in navigation and inspection simulations.