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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.19532 |
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| _version_ | 1866913450255450112 |
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| author | Eaton, Kenneth Balloch, Jonathan Kim, Julia Riedl, Mark |
| author_facet | Eaton, Kenneth Balloch, Jonathan Kim, Julia Riedl, Mark |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19532 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited Eaton, Kenneth Balloch, Jonathan Kim, Julia Riedl, Mark Artificial Intelligence Machine Learning 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. |
| title | The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.19532 |