Triston Grayston

Mechanistic Interpretability of Reinforcement Learning Agents

2024 · arXiv (Cornell University)

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 …