  In this paper we apply guided policy search (GPS) based reinforcement
learning framework for a high dimensional optimal control problem arising in an
additive manufacturing process. The problem comprises of controlling the
process parameters so that layer-wise deposition of material leads to desired
geometric characteristics of the resulting part surface while minimizing the
material deposited. A realistic simulation model of the deposition process
along with carefully selected set of guiding distributions generated based on
iterative Linear Quadratic Regulator is used to train a neural network policy
using GPS. A closed loop control based on the trained policy and in-situ
measurement of the deposition profile is tested experimentally, and shows
promising performance.
