  We study how a Reinforcement Learning (RL) system can remain sample-efficient
when learning from an imperfect model of the environment. This is particularly
challenging when the learning system is resource-constrained and in continual
settings, where the environment dynamics change. To address these challenges,
our paper introduces an online, meta-gradient algorithm that tunes a
probability with which states are queried during Dyna-style planning. Our study
compares the aggregate, empirical performance of this meta-gradient method to
baselines that employ conventional sampling strategies. Results indicate that
our method improves efficiency of the planning process, which, as a
consequence, improves the sample-efficiency of the overall learning process. On
the whole, we observe that our meta-learned solutions avoid several pathologies
of conventional planning approaches, such as sampling inaccurate transitions
and those that stall credit assignment. We believe these findings could prove
useful, in future work, for designing model-based RL systems at scale.
