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Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space

Sindre Benjamin Remman, Anastasios M. Lekkas

TL;DR

The paper tackles the opacity of deep reinforcement learning policies by analyzing their latent-space trajectories through an unsupervised pipeline that combines PaCMAP for dimensionality reduction with TRACLUS for trajectory clustering. By applying this approach to a MountainCarContinuous-v0 policy, the authors identify distinct behavior modes and suboptimal regions, then leverage domain knowledge to implement simple policy adjustments that yield measurable performance gains. Key contributions include a practical workflow for uncovering behavior modes in DRL policies, showing that clustering in a reduced latent space can reveal finer structure and actionable improvements. The findings demonstrate the method's potential to augment interpretability and guide targeted policy enhancements in control tasks.

Abstract

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.

Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space

TL;DR

The paper tackles the opacity of deep reinforcement learning policies by analyzing their latent-space trajectories through an unsupervised pipeline that combines PaCMAP for dimensionality reduction with TRACLUS for trajectory clustering. By applying this approach to a MountainCarContinuous-v0 policy, the authors identify distinct behavior modes and suboptimal regions, then leverage domain knowledge to implement simple policy adjustments that yield measurable performance gains. Key contributions include a practical workflow for uncovering behavior modes in DRL policies, showing that clustering in a reduced latent space can reveal finer structure and actionable improvements. The findings demonstrate the method's potential to augment interpretability and guide targeted policy enhancements in control tasks.

Abstract

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.
Paper Structure (14 sections, 5 equations, 6 figures, 1 algorithm)

This paper contains 14 sections, 5 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. TRACLUS
  4. PaCMAP
  5. Methodology
  6. Training the policy and generating the data set
  7. Dimensionality reduction
  8. Trajectory clustering
  9. Behavior classification and Policy improvement
  10. Results and Discussion
  11. Clustering in latent space vs reduced latent space
  12. Behavioral clustering of the MC Policy
  13. Improvement of the MC Policy based on behavior clustering
  14. Conclusion

Figures (6)

  • Figure 1: MountainCarContinuous-v0 environment
  • Figure 2: Results from clustering the MC Policy in non-reduced latent space. All lines represent trajectories, where each point in each trajectory corresponds to one time step in an episode of the agent operating in the environment. All trajectories end at the goal car position, which is when position $>= 0.45$, as seen in the upper-right of the plot.
  • Figure 3: Results from clustering the MC Policy in the reduced latent space. Because of the high number of clusters, this result is shown in two plots for easier visualization.
  • Figure 4: Zoomed in on the area of the state space where the boundary between having enough mechanical energy to reach the goal directly and not having enough mechanical energy exists. Numbers are next to the start of each trajectory/episode we want to discuss for ease of referring to them.
  • Figure 5: The first plot shows running the MC Policy in the environment from the initial state $s_0 = [-0.35, 0.028]$. The second plot shows the same, with the only change being that we force the first action to be $a_0 = -1$.
  • ...and 1 more figures