  In this work, we use optimal control to change the behavior of a deep
reinforcement learning policy by optimizing directly in the policy's latent
space. We hypothesize that distinct behavioral patterns, termed behavioral
modes, can be identified within certain regions of a deep reinforcement
learning policy's latent space, meaning that specific actions or strategies are
preferred within these regions. We identify these behavioral modes using latent
space dimension-reduction with \ac*{pacmap}. Using the actions generated by the
optimal control procedure, we move the system from one behavioral mode to
another. We subsequently utilize these actions as a filter for interpreting the
neural network policy. The results show that this approach can impose desired
behavioral modes in the policy, demonstrated by showing how a failed episode
can be made successful and vice versa using the lunar lander reinforcement
learning environment.
