  Explainable reinforcement learning allows artificial agents to explain their
behavior in a human-like manner aiming at non-expert end-users. An efficient
alternative of creating explanations is to use an introspection-based method
that transforms Q-values into probabilities of success used as the base to
explain the agent's decision-making process. This approach has been effectively
used in episodic and discrete scenarios, however, to compute the probability of
success in non-episodic and more complex environments has not been addressed
yet. In this work, we adapt the introspection method to be used in a
non-episodic task and try it in a continuous Atari game scenario solved with
the Rainbow algorithm. Our initial results show that the probability of success
can be computed directly from the Q-values for all possible actions.
