saturated and every value smaller than 0 is set to 0, and every value above 1 is set to 1.

The probability is calculated based on how well that Q-value scales towards the maximum reward. In the Chef’s Hat game, the agent only reaches the final reward if it wins a game. Also, for each action which is not the final reward, the agent gets a -0.01 reward in order to encourage the agent to prefer shorter paths to the final reward [@barrosICPR2020]. This impacts directly on the final reward calculation, as for each new turn the agent is playing, the final reward changes. To take this into consideration, we update the final reward per turn following:

$$R^{T} = 1 - \left( T\times0.01 \right )$$

where $T$ represents the current turn on that specific game.

A Moody neural network
----------------------

The observation of the Q-values on the Chef’s Hat scenario already allowed for an understanding of how different learning mechanisms impacted on the final performance of each agent during a series of games [@barros2020chef]. Given the Chef’s Hat specific greedy policy, however, each action that an agent selects is limited by the possible actions given that specific state. There is 