AI Safety Papers

Mechanistic Interpretability of Reinforcement Learning Agents

Authors

Tristan Trim, Triston Grayston

Abstract

This paper explores the mechanistic interpretability of reinforcement learning (RL) agents through an analysis of a neural network trained on procedural maze environments. By dissecting the network's inner workings, we identified fundamental features like maze walls and pathways, forming the basis of the model's decision-making process. A significant observation was the goal misgeneralization, where the RL agent developed biases towards certain navigation strategies, such as consistently moving towards the top right corner, even in the absence of explicit goals. Using techniques like saliency mapping and feature mapping, we visualized these biases. We furthered this exploration with the development of novel tools for interactively exploring layer activations.

Publication Details

  • Published: Unknown
  • Added to AI Safety Papers: 2024-11-11
  • Original Paper: Link

Summary

This paper explores the mechanistic interpretability of reinforcement learning (RL) agents by analyzing a neural network trained on procedural maze environments. The research focuses on understanding how the network processes information and makes decisions, with particular attention to goal misgeneralization. Key aspects of the study:

  1. Goal Misgeneralization Analysis

[See Figure 1 in the original paper]

The researchers examined how RL agents can develop biases in their navigation strategies, particularly the tendency to move toward the top-right corner even when the goal is elsewhere. This figure effectively illustrates different scenarios of goal misgeneralization and robust agent behavior.

  1. Maze Environment

[See Figure 2 in the original paper]

The study utilized procedurally generated mazes, with both upscaled (512x512x3) and pre-processed (64x64x3) versions used for training and analysis. These mazes provided a consistent environment for testing agent behavior and decision-making processes.

  1. Feature Mapping

[See Figure 3 in the original paper]

The researchers analyzed the first convolutional layer’s activations, revealing how the network processes basic features like walls, paths, and corners. The visualization shows higher activations in green, demonstrating the network’s attention to specific maze elements.

  1. Interactive Analysis Tools

[See Figure 13 in the original paper]

[See Figure 14 in the original paper]

The team developed novel tools for analysis: - PixCol: An interactive pixel class coloring tool - NDSP: An n-dimensional scatter plot tool for distribution classification These tools helped identify and correct misclassifications in the network’s representations. Key Findings:

  1. The network develops distinct representations for basic maze features (walls, corners, paths) in early layers

  2. Goal misgeneralization manifests as a bias toward the top-right corner, even when suboptimal

  3. Deeper layers show increasingly abstract representations of the maze environment

  4. The mouse (agent) and cheese (goal) are processed differently by the network, with the mouse receiving more distinct representation The research contributes to AI interpretability by providing detailed insights into how RL agents process and represent spatial information, make decisions, and develop biases during training. The study also demonstrates the value of combining multiple interpretability techniques for comprehensive analysis. The paper’s methodology and tools provide a framework for future research in understanding and improving RL agent behavior, particularly in addressing goal misgeneralization issues.