In the traditional reinforcement learning problem an agent acts in a stochastic environment by choosing a series of actions over a set of time steps in order to maximize the total cumulative reward. This problem is modeled as a Markov Decision Process (MDP) consisting of a state space S, an action space A, an initial state distribution with probability density p 0 (s 0 ), a conditional transition probability with density p(s t+1 |s t , a t ), and a reward function r : S × A → R .