  Model-based reinforcement learning (MBRL) has been shown to be a powerful
framework for data-efficiently learning control of continuous tasks. Recent
work in MBRL has mostly focused on using more advanced function approximators
and planning schemes, with little development of the general framework. In this
paper, we identify a fundamental issue of the standard MBRL framework -- what
we call the objective mismatch issue. Objective mismatch arises when one
objective is optimized in the hope that a second, often uncorrelated, metric
will also be optimized. In the context of MBRL, we characterize the objective
mismatch between training the forward dynamics model w.r.t.~the likelihood of
the one-step ahead prediction, and the overall goal of improving performance on
a downstream control task. For example, this issue can emerge with the
realization that dynamics models effective for a specific task do not
necessarily need to be globally accurate, and vice versa globally accurate
models might not be sufficiently accurate locally to obtain good control
performance on a specific task. In our experiments, we study this objective
mismatch issue and demonstrate that the likelihood of one-step ahead
predictions is not always correlated with control performance. This observation
highlights a critical limitation in the MBRL framework which will require
further research to be fully understood and addressed. We propose an initial
method to mitigate the mismatch issue by re-weighting dynamics model training.
Building on it, we conclude with a discussion about other potential directions
of research for addressing this issue.

Machine Learning;Robotics;Machine Learning