  As deep reinforcement learning driven by visual perception becomes more
widely used there is a growing need to better understand and probe the learned
agents. Understanding the decision making process and its relationship to
visual inputs can be very valuable to identify problems in learned behavior.
However, this topic has been relatively under-explored in the research
community. In this work we present a method for synthesizing visual inputs of
interest for a trained agent. Such inputs or states could be situations in
which specific actions are necessary. Further, critical states in which a very
high or a very low reward can be achieved are often interesting to understand
the situational awareness of the system as they can correspond to risky states.
To this end, we learn a generative model over the state space of the
environment and use its latent space to optimize a target function for the
state of interest. In our experiments we show that this method can generate
insights for a variety of environments and reinforcement learning methods. We
explore results in the standard Atari benchmark games as well as in an
autonomous driving simulator. Based on the efficiency with which we have been
able to identify behavioural weaknesses with this technique, we believe this
general approach could serve as an important tool for AI safety applications.
