Reasoning and Contextualization (Machine Learning & AI Project)

Build an agent that plays a simplified spectrum sharing game with an agent provided by TEST. Your agent is called the Player agent A; the TEST agent is called D. The game consists of many turns in which each agent will vie for spectrum resources (e.g., channels) represented by integers. In each turn, two things happen simultaneously: • The TEST agent makes its choice of spectrum resource by choosing an integer between 1 and M: its output D0. • The Player agent emits a pair of integers: its output A0 (its choice of spectrum resource) and its prediction of the TEST output D0 both between 1 and M. After these actions, A gets a payoff for avoiding a spectrum resource collision (A0 ≠ D0). A increases its payoff if it predicts D’s exact selection (D0 = D0) since successful prediction enables optimal spectrum sharing. This is repeated over many turns. For A to do well, it must observe the history of D’s behavior and improve its ability to predict D’s actions.