Before revision:  The expressive power of LTL supports the specification of a diversity of complex temporally extended behaviours that include conditionals and alternative realizations  . Experiments on discrete and continuous domains demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.
After revision:  The expressive power of LTL supports the specification of a diversity of complex temporally extended behaviours that include conditionals and alternative realizations . To reduce the overhead of learning LTL semantics, we introduce an environment-agnostic LTL pretraining scheme which improves sample-efficiency in downstream environments . Experiments on discrete and continuous domains demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.