  Direct policy search is one of the most important algorithm of reinforcement
learning. However, learning from scratch needs a large amount of experience
data and can be easily prone to poor local optima. In addition to that, a
partially trained policy tends to perform dangerous action to agent and
environment. In order to overcome these challenges, this paper proposed a
policy initialization algorithm called Policy Learning based on Completely
Behavior Cloning (PLCBC). PLCBC first transforms the Model Predictive Control
(MPC) controller into a piecewise affine (PWA) function using multi-parametric
programming, and uses a neural network to express this function. By this way,
PLCBC can completely clone the MPC controller without any performance loss, and
is totally training-free. The experiments show that this initialization
strategy can help agent learn at the high reward state region, and converge
faster and better.
