where D j,t is a set of adjacent nodes to node j observed up to iteration step t, each of which connects to node j via one reaction path, and including k = j itself. v N j,t denotes the total number of such adjacent nodes, that is, the degree of node j, at iteration step t. According to this definition, if no adjacent nodes are available in this iteration step, the average score (s j,t ) equals the score (s j ) of node j itself, which is a prior expectation for sampling. Notably, the scores of adjacent nodes also correspond to the rewards at node j as in usual reinforcement learning. In the next section, various node selection policies employing the average score and standard deviation are introduced.