of the 200 actions will have, given a certain state, to reach the maximal goal. For a given state, the agent calculates which action would give it the highest probability of reaching the end state. The Q-values reading, however, does not allow us to establish how closely the agent is of winning the game by choosing that action, and thus, does not give us a better understanding of the agents’ performance, but only of which action was the most appropriate given that specific state.

Calculating Confidence
----------------------

To address the problem of calculating the impact of the selected action towards reaching the final goal, the introspection-based transformation of the Q-values [@cruz2020explainable] was proposed. This approach focuses on scaling the selected Q-value towards the final goal using a logarithm transformation which computes the probability of success, that we use as a confidence ($C$), that this specific action will lead the agent to reach the final state:

$$C = \left ( \frac{1}{2} \times log_{10}  \frac{Q(s,a)}{R^{T}} \right )$$

where $Q(s,a)$ is the Q-value of a selected action, and $R^{T}$ is the maximum reward achieved by the agent at that specific time step. To normalize the probability between the interval $[0,1]$, the probability is 