Human-Allied Relational Reinforcement Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908600572575744 |
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| author | Darvishvand, Fateme Golivand Shindo, Hikaru Sidheekh, Sahil Kersting, Kristian Natarajan, Sriraam |
| author_facet | Darvishvand, Fateme Golivand Shindo, Hikaru Sidheekh, Sahil Kersting, Kristian Natarajan, Sriraam |
| contents | Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of propositional tasks ignoring the inherent structure that exists in the problem. Consequently, relational extensions (RRL) have been developed for such structured problems that allow for effective generalization to arbitrary number of objects. However, they inherently make strong assumptions about the problem structure. We introduce a novel framework that combines RRL with object-centric representation to handle both structured and unstructured data. We enhance learning by allowing the system to actively query the human expert for guidance by explicitly modeling the uncertainty over the policy. Our empirical evaluation demonstrates the effectiveness and efficiency of our proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human-Allied Relational Reinforcement Learning Darvishvand, Fateme Golivand Shindo, Hikaru Sidheekh, Sahil Kersting, Kristian Natarajan, Sriraam Machine Learning Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of propositional tasks ignoring the inherent structure that exists in the problem. Consequently, relational extensions (RRL) have been developed for such structured problems that allow for effective generalization to arbitrary number of objects. However, they inherently make strong assumptions about the problem structure. We introduce a novel framework that combines RRL with object-centric representation to handle both structured and unstructured data. We enhance learning by allowing the system to actively query the human expert for guidance by explicitly modeling the uncertainty over the policy. Our empirical evaluation demonstrates the effectiveness and efficiency of our proposed approach. |
| title | Human-Allied Relational Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.16188 |