  Standard model-based reinforcement learning (MBRL) approaches fit a
transition model of the environment to all past experience, but this wastes
model capacity on data that is irrelevant for policy improvement. We instead
propose a new "transition occupancy matching" (TOM) objective for MBRL model
learning: a model is good to the extent that the current policy experiences the
same distribution of transitions inside the model as in the real environment.
We derive TOM directly from a novel lower bound on the standard reinforcement
learning objective. To optimize TOM, we show how to reduce it to a form of
importance weighted maximum-likelihood estimation, where the automatically
computed importance weights identify policy-relevant past experiences from a
replay buffer, enabling stable optimization. TOM thus offers a plug-and-play
model learning sub-routine that is compatible with any backbone MBRL algorithm.
On various Mujoco continuous robotic control tasks, we show that TOM
successfully focuses model learning on policy-relevant experience and drives
policies faster to higher task rewards than alternative model learning
approaches.
