  Deep Reinforcement Learning (DRL) is a trending field of research, showing
great promise in many challenging problems such as playing Atari, solving Go
and controlling robots. While DRL agents perform well in practice we are still
missing the tools to analayze their performance and visualize the temporal
abstractions that they learn. In this paper, we present a novel method that
automatically discovers an internal Semi Markov Decision Process (SMDP) model
in the Deep Q Network's (DQN) learned representation. We suggest a novel
visualization method that represents the SMDP model by a directed graph and
visualize it above a t-SNE map. We show how can we interpret the agent's policy
and give evidence for the hierarchical state aggregation that DQNs are learning
automatically. Our algorithm is fully automatic, does not require any domain
specific knowledge and is evaluated by a novel likelihood based evaluation
criteria.
