LAUSR

In this work, we build upon the observation that offline reinforcement learning (RL) is synergistic with task hierarchies that decompose large Markov decision processes (MDPs). Task hierarchies can allow more efficient sample collection from large MDPs, while offline algorithms can learn better policies than the so-called “recursively optimal” or even hierarchically optimal policies learned by standard hierarchical RL algorithms. To enable this synergy, we study sample collection strategies for offline RL that are consistent with a provided task hierarchy while still providing good exploration of the state-action space. We show that naïve extensions of uniform random sampling do not work well in this case and design a strategy that has provably good convergence properties. We also augment the initial set of samples using additional information from the task hierarchy, such as state abstraction. We use the augmented set of samples to learn a policy offline. Given a capable offline RL algorithm, this policy is then guaranteed to have a value greater than or equal to the value of the hierarchically optimal policy. We evaluate our approach on several domains and show that samples generated using a task hierarchy with a suitable strategy allow significantly more sample-efficient convergence than standard offline RL. Further, our approach also shows more sample-efficient convergence to policies with value greater than or equal to hierarchically optimal policies found through an online hierarchical RL approach.