We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. The combinatorial task sets we target consist of up to ~10^{39 a well-known formal language -- linear temporal logic (LTL) -- to specify instructions, using a domain-specific vocabulary. We propose a novel approach to learning that exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training. 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 \sim10 demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.
We address the problem of teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments. Instructions are expressed in a well-known formal language -- linear temporal logic (LTL) -- and can specify a diversity of complex, temporally extended behaviours, including conditionals and alternative realizations. Our proposed learning approach exploits the compositional syntax and the semantics of LTL, enabling our RL agent to learn task-conditioned policies that generalize to new instructions, not observed during training. 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 target combinatorial task sets of up to\sim10^{39 demonstrate the strength of our approach in learning to solve (unseen) tasks, given LTL instructions.
R
The combinatorial task sets we target consist of up to ~10^{39
Instructions are expressed in
R
to specify instructions, using a domain-specific vocabulary. We propose a novel approach to learning that
and can specify a diversity of complex, temporally extended behaviours, including conditionals and alternative realizations. Our proposed learning approach
D
The expressive power of LTL supports the specification of a diversity of complex temporally extended behaviours that include conditionals and alternative realizations.
A
target combinatorial task sets of up to
A
^{39