that happen by the end of the game, as explained in the analysis made by Barros et al. [@barros2020chef]. Most importantly, we observe that, in this game, the last action performed by DQL was assessed as having strong confidence. The mood readings map this behavior clearly, presenting a higher pleasure and arousal reading by the end of the match, which is enhanced by this agent winning the match.

The estimation made by PPO-based about the DQL-based agent, at first, seems different from the estimations obtained by DQL-based agent itself. However, once understanding how the PPO agent learns to play the game, as explained in detail by Barros et al. [@barros2020chef], the estimations seem to make more sense. The PPO-based agent develops during training a strategy that enforces it to have higher confidence in its actions at the beginning of the game when compared to the end of the game. That means the PPO-based agent evaluates the actions from the DQL-based agent using its understanding of what good actions are. And thus, presents a very particular version of DQL-based agents’ confidence, disregarding the last action as having a higher impact.

Doing the same observation on the plots about the PPO-based agent, 