  Reinforcement learning is a machine learning approach based on behavioral
psychology. It is focused on learning agents that can acquire knowledge and
learn to carry out new tasks by interacting with the environment. However, a
problem occurs when reinforcement learning is used in critical contexts where
the users of the system need to have more information and reliability for the
actions executed by an agent. In this regard, explainable reinforcement
learning seeks to provide to an agent in training with methods in order to
explain its behavior in such a way that users with no experience in machine
learning could understand the agent's behavior. One of these is the
memory-based explainable reinforcement learning method that is used to compute
probabilities of success for each state-action pair using an episodic memory.
In this work, we propose to make use of the memory-based explainable
reinforcement learning method in a hierarchical environment composed of
sub-tasks that need to be first addressed to solve a more complex task. The end
goal is to verify if it is possible to provide to the agent the ability to
explain its actions in the global task as well as in the sub-tasks. The results
obtained showed that it is possible to use the memory-based method in
hierarchical environments with high-level tasks and compute the probabilities
of success to be used as a basis for explaining the agent's behavior.
