  To enhance the interpretability of Reinforcement Learning (RL), we propose
Revealing Evolutionary Action Consequence Trajectories (REACT). In contrast to
the prevalent practice of validating RL models based on their optimal behavior
learned during training, we posit that considering a range of edge-case
trajectories provides a more comprehensive understanding of their inherent
behavior. To induce such scenarios, we introduce a disturbance to the initial
state, optimizing it through an evolutionary algorithm to generate a diverse
population of demonstrations. To evaluate the fitness of trajectories, REACT
incorporates a joint fitness function that encourages both local and global
diversity in the encountered states and chosen actions. Through assessments
with policies trained for varying durations in discrete and continuous
environments, we demonstrate the descriptive power of REACT. Our results
highlight its effectiveness in revealing nuanced aspects of RL models' behavior
beyond optimal performance, thereby contributing to improved interpretability.
