have to deal with the interactions between the agents themselves. Some of the most common applications for competitive reinforcement learning involve the design and implementation of learning agents in simulations that simplify several aspects of the real world such as the implementation of grid-based autonomous vehicles [@fridman2018deeptraffic], life-simulation/resources gathering [@xu2018hierarchical], and multi-player games [@mckenzie2017competitive]. In these cases, explaining the agents’ behavior based on the simplified environment is somehow possible, but they would not scale for real-world applications.

In this regard, we propose a novel methodology to explain the behavior of artificial agents in real-world modeling a competitive scenario. We deploy our experiments onto the multiplayer Chef’s Hat card game [@barros2020chef] as it offers a dynamic interaction between the players and simulates directly the real-world counterpart game. In the scenarios’ baseline, four agents play against each other and their performance is measured directly based on how many games they win [@barrosICPR2020]. It is not possible, however, without an extensive manual observation of their action-selection pattern to explain their winning behavior while the game happens.

To identify the impact of each selected action, we propose the *Moody framework* which uses the agents’ own evaluation of the game to provide a temporal reference 