In RL-CSP the set of actions is discrete and consists of the limited number of n actions , … , . These actions are the set of moves specific to the CSP code that the algorithm uses to pass from one structure to another. In reinforcement learning, the state space is defined as the set of all possible states that the algorithm can visit. In this work, the state is parameterized by only one feature, its calculated energy ( ) that is defined per atom to allow the comparison between structures containing different numbers of atoms and scaled via Z-score normalization . The policy ( ) is a set of rules that select the next action depending on the current state . This policy is probabilistic as it provides the probability distribution of the actions to be selected from. The policy ( ) is parameterized by the learned policy vector = ( , … , ) holding two numbers , for each action . The policy vector is updated after each action and was either accepted or rejected by the BH algorithm according to the reward ( , ′, ), the number representing the benefit of moving from state to state following action .