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The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited

Kenneth Eaton, Jonathan Balloch, Julia Kim, Mark Riedl

TL;DR

The paper interrogates whether vector quantization (VQ) yields interpretable latent representations in model-based reinforcement learning. Using the IRIS MBRL framework and Grad-CAM analyses in the Crafter environment, the study assesses code consistency and grounding across a large dataset. Findings show that most VQ codes produce zero or unstable, non-grounded heatmaps, with only a handful of codes exhibiting weak, inconsistent semantic associations, and co-occurrence effects are rare and often episode-specific. The work concludes that VQ alone is insufficient for interpretability in MBRL and suggests that latent semantic alignment is necessary for robust grounding of latent codes.

Abstract

Interpretability of deep reinforcement learning systems could assist operators with understanding how they interact with their environment. Vector quantization methods -- also called codebook methods -- discretize a neural network's latent space that is often suggested to yield emergent interpretability. We investigate whether vector quantization in fact provides interpretability in model-based reinforcement learning. Our experiments, conducted in the reinforcement learning environment Crafter, show that the codes of vector quantization models are inconsistent, have no guarantee of uniqueness, and have a limited impact on concept disentanglement, all of which are necessary traits for interpretability. We share insights on why vector quantization may be fundamentally insufficient for model interpretability.

The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited

TL;DR

The paper interrogates whether vector quantization (VQ) yields interpretable latent representations in model-based reinforcement learning. Using the IRIS MBRL framework and Grad-CAM analyses in the Crafter environment, the study assesses code consistency and grounding across a large dataset. Findings show that most VQ codes produce zero or unstable, non-grounded heatmaps, with only a handful of codes exhibiting weak, inconsistent semantic associations, and co-occurrence effects are rare and often episode-specific. The work concludes that VQ alone is insufficient for interpretability in MBRL and suggests that latent semantic alignment is necessary for robust grounding of latent codes.

Abstract

Interpretability of deep reinforcement learning systems could assist operators with understanding how they interact with their environment. Vector quantization methods -- also called codebook methods -- discretize a neural network's latent space that is often suggested to yield emergent interpretability. We investigate whether vector quantization in fact provides interpretability in model-based reinforcement learning. Our experiments, conducted in the reinforcement learning environment Crafter, show that the codes of vector quantization models are inconsistent, have no guarantee of uniqueness, and have a limited impact on concept disentanglement, all of which are necessary traits for interpretability. We share insights on why vector quantization may be fundamentally insufficient for model interpretability.
Paper Structure (9 sections, 4 figures, 4 tables)

This paper contains 9 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Experiments in VQ Interpretability
  4. Experimental Approach.
  5. Results.
  6. Discussion
  7. Code Frequency
  8. Consistent and Inconsistent Code Crops
  9. Code Co-occurrence

Figures (4)

  • Figure 1: IRIS transition model architecture and our evaluation process. Heatmaps from Grad-CAM crop the input image to focus regions, which are then embedded by a pre-trained encoder.
  • Figure 2: The data from our GradCAM experiments show that VQ codes are inconsistent quantitatively (low mean cosine similarity) and qualitatively (in the lack of t-SNE separation).
  • Figure 3: Percentage of each code's occurrence in the dataset.
  • Figure 4: Co-occurrence rate for the ten most frequently co-occurring pairs of codes