The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
<|reference_start|>The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited: 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.<|reference_end|>
@article{eaton2024the,
    title={The Interpretability of Codebooks in Model-Based Reinforcement Learning
  is Limited},
    author={Kenneth Eaton, Jonathan Balloch, Julia Kim, Mark Riedl},
    journal={arXiv preprint arXiv:2407.19532},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19532},
    primaryClass={cs.AI cs.LG}
}