  Understanding the behavior of deep reinforcement learning (DRL) agents is
crucial for improving their performance and reliability. However, the
complexity of their policies often makes them challenging to understand. In
this paper, we introduce a new approach for investigating the behavior modes of
DRL policies, which involves utilizing dimensionality reduction and trajectory
clustering in the latent space of neural networks. Specifically, we use
Pairwise Controlled Manifold Approximation Projection (PaCMAP) for
dimensionality reduction and TRACLUS for trajectory clustering to analyze the
latent space of a DRL policy trained on the Mountain Car control task. Our
methodology helps identify diverse behavior patterns and suboptimal choices by
the policy, thus allowing for targeted improvements. We demonstrate how our
approach, combined with domain knowledge, can enhance a policy's performance in
specific regions of the state space.
