  Explaining the behavior of reinforcement learning agents operating in
sequential decision-making settings is challenging, as their behavior is
affected by a dynamic environment and delayed rewards. Methods that help users
understand the behavior of such agents can roughly be divided into local
explanations that analyze specific decisions of the agents and global
explanations that convey the general strategy of the agents. In this work, we
study a novel combination of local and global explanations for reinforcement
learning agents. Specifically, we combine reward decomposition, a local
explanation method that exposes which components of the reward function
influenced a specific decision, and HIGHLIGHTS, a global explanation method
that shows a summary of the agent's behavior in decisive states. We conducted
two user studies to evaluate the integration of these explanation methods and
their respective benefits. Our results show significant benefits for both
methods. In general, we found that the local reward decomposition was more
useful for identifying the agents' priorities. However, when there was only a
minor difference between the agents' preferences, then the global information
provided by HIGHLIGHTS additionally improved participants' understanding.
