reward for the state ($s_t$) and action ($a_t$) association, $\gamma$ represents the discount factor, a modulator that estimates the importance of the future rewards, and $maxQ\left (s_{t+1}, a_t \right )$ is the estimation of Q-value for the next state.

A *Moody Framework* 
====================

To evaluate the performance of the agents when playing Chef’s Hat is not a simple task. At first, state-based evaluations could give us a straight answer to performance measures, but they do need external evaluation and they would be completely biased on the evaluators’ knowledge about the game. Counting the number of cards a player has at hand at a specific moment can indicate a precise performance value, but it can vary drastically depending on the players’ own strategy. The same goes for measuring number of discarded cards in each turn.

Having a self-evaluation of the agents’ own performance, thus, could solve this problem. As the action-selection strategy of the agent is made based on its own knowledge, the agent will be able to explain its own actions based on its own judgment.

One of the simplest ways of explaining the action-selection impact of a player is a Q-value-based observation. The Q-value represents the impact that each 