action selection by applying a mask on the final Q-values composed of only the currently allowed actions, given a certain state.

As discussed by Barros et al. [@barrosICPR2020], the learning agents learn different strategies when trained to play the Chef’s Hat game. While the DQL agent learns a set of restricted actions, which are mostly deployed by the end of the match, the PPO agent usually has higher confidence in winning the game when using a set of actions at the beginning of the match.

In this research, we are interested in establishing an explanation on how each of these agents performs while playing Chef’s Hat, so we do not focus or detail the agents’ learning processes. To guarantee that each agent has its own strategies when playing the game, we follow the baseline protocols established by the *vs Everyone* self-play routine [@barrosICPR2020]. Figure \[fig:models\] illustrates our implemented agents.

![The detailed implementation of the DQL and the PPO agents.[]{data-label="fig:models"}](models.png){width="0.65\columnwidth"}

The Principles of Q-Learning
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Both of our agents implement a Q-learning routine. The general Q-learning algorithm learns to maximize the probability of choosing an action that leads to maximum reward. For that, it calculates a Q-value (quality value) for each 