Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911885623820288 |
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| author | Kohler, Hector Delfosse, Quentin Akrour, Riad Kersting, Kristian Preux, Philippe |
| author_facet | Kohler, Hector Delfosse, Quentin Akrour, Riad Kersting, Kristian Preux, Philippe |
| contents | Deep reinforcement learning agents are prone to goal misalignments. The black-box nature of their policies hinders the detection and correction of such misalignments, and the trust necessary for real-world deployment. So far, solutions learning interpretable policies are inefficient or require many human priors. We propose INTERPRETER, a fast distillation method producing INTerpretable Editable tRee Programs for ReinforcEmenT lEaRning. We empirically demonstrate that INTERPRETER compact tree programs match oracles across a diverse set of sequential decision tasks and evaluate the impact of our design choices on interpretability and performances. We show that our policies can be interpreted and edited to correct misalignments on Atari games and to explain real farming strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14956 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning Kohler, Hector Delfosse, Quentin Akrour, Riad Kersting, Kristian Preux, Philippe Artificial Intelligence Machine Learning Deep reinforcement learning agents are prone to goal misalignments. The black-box nature of their policies hinders the detection and correction of such misalignments, and the trust necessary for real-world deployment. So far, solutions learning interpretable policies are inefficient or require many human priors. We propose INTERPRETER, a fast distillation method producing INTerpretable Editable tRee Programs for ReinforcEmenT lEaRning. We empirically demonstrate that INTERPRETER compact tree programs match oracles across a diverse set of sequential decision tasks and evaluate the impact of our design choices on interpretability and performances. We show that our policies can be interpreted and edited to correct misalignments on Atari games and to explain real farming strategies. |
| title | Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.14956 |