  Model-based Reinforcement Learning (MBRL) allows data-efficient learning
which is required in real world applications such as robotics. However, despite
the impressive data-efficiency, MBRL does not achieve the final performance of
state-of-the-art Model-free Reinforcement Learning (MFRL) methods. We leverage
the strengths of both realms and propose an approach that obtains high
performance with a small amount of data. In particular, we combine MFRL and
Model Predictive Control (MPC). While MFRL's strength in exploration allows us
to train a better forward dynamics model for MPC, MPC improves the performance
of the MFRL policy by sampling-based planning. The experimental results in
standard continuous control benchmarks show that our approach can achieve
MFRL`s level of performance while being as data-efficient as MBRL.
