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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.09761 |
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| _version_ | 1866910017800634368 |
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| author | Pannacci, Matteo Fanti, Andrea Umili, Elena Capobianco, Roberto |
| author_facet | Pannacci, Matteo Fanti, Andrea Umili, Elena Capobianco, Roberto |
| contents | In this work we address the problem of training a Reinforcement Learning agent to follow multiple temporally-extended instructions expressed in Linear Temporal Logic in sub-symbolic environments. Previous multi-task work has mostly relied on knowledge of the mapping between raw observations and symbols appearing in the formulae. We drop this unrealistic assumption by jointly training a multi-task policy and a symbol grounder with the same experience. The symbol grounder is trained only from raw observations and sparse rewards via Neural Reward Machines in a semi-supervised fashion. Experiments on vision-based environments show that our method achieves performance comparable to using the true symbol grounding and significantly outperforms state-of-the-art methods for sub-symbolic environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_09761 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization Pannacci, Matteo Fanti, Andrea Umili, Elena Capobianco, Roberto Machine Learning Artificial Intelligence In this work we address the problem of training a Reinforcement Learning agent to follow multiple temporally-extended instructions expressed in Linear Temporal Logic in sub-symbolic environments. Previous multi-task work has mostly relied on knowledge of the mapping between raw observations and symbols appearing in the formulae. We drop this unrealistic assumption by jointly training a multi-task policy and a symbol grounder with the same experience. The symbol grounder is trained only from raw observations and sparse rewards via Neural Reward Machines in a semi-supervised fashion. Experiments on vision-based environments show that our method achieves performance comparable to using the true symbol grounding and significantly outperforms state-of-the-art methods for sub-symbolic environments. |
| title | Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.09761 |