  Reformulating the history matching problem from a least-square mathematical
optimization problem into a Markov Decision Process introduces a method in
which reinforcement learning can be utilized to solve the problem. This method
provides a mechanism where an artificial deep neural network agent can interact
with the reservoir simulator and find multiple different solutions to the
problem. Such formulation allows for solving the problem in parallel by
launching multiple concurrent environments enabling the agent to learn
simultaneously from all the environments at once, achieving significant speed
up.
