  Substantial advancements to model-based reinforcement learning algorithms
have been impeded by the model-bias induced by the collected data, which
generally hurts performance. Meanwhile, their inherent sample efficiency
warrants utility for most robot applications, limiting potential damage to the
robot and its environment during training. Inspired by information theoretic
model predictive control and advances in deep reinforcement learning, we
introduce Model Predictive Actor-Critic (MoPAC), a hybrid
model-based/model-free method that combines model predictive rollouts with
policy optimization as to mitigate model bias. MoPAC leverages optimal
trajectories to guide policy learning, but explores via its model-free method,
allowing the algorithm to learn more expressive dynamics models. This
combination guarantees optimal skill learning up to an approximation error and
reduces necessary physical interaction with the environment, making it suitable
for real-robot training. We provide extensive results showcasing how our
proposed method generally outperforms current state-of-the-art and conclude by
evaluating MoPAC for learning on a physical robotic hand performing valve
rotation and finger gaiting--a task that requires grasping, manipulation, and
then regrasping of an object.
