the mood, we also exhibit the same measures for 10 games played in a row.

Self vs Estimated
-----------------

To evaluate how well an agent can estimate the performance of another agent within the same game, we run 100 games where 2 DQL-based agents play against 2 PPO-based agents. We calculate the correlation between the mood and confidences and the estimated mood and confidences obtained by each of the agents about themselves. Using two agents with the same learning algorithm playing against each other will allow us to evaluate also the impact of the learned strategies on how to play the game on the estimated values.

Results and Discussions
=======================

Mood vs Confidence
------------------

![Confidence and mood of the DQL-based agent, and e-confidence and e-mood estimated by PPO-based agent for 1 game won by the DQL-based agent.[]{data-label="fig:dqlM1"}](hyp1Results_DQL.png){width="1\linewidth"}

The winner of the single match was the DQL agent. It won after performing 26 actions. The confidence and mood of the DQL-based agent are exhibited in Figure \[fig:dqlM1\], together with the estimation of the DQL confidences and mood obtained by the PPO-based agent. It is easy to see the inherent behavior enforced by the DQL algorithm on showing high confidence on actions 