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This research advances reinforcement learning by enabling agents to follow complex temporal logic instructions in sub-symbolic environments without assuming prior knowledge of symbol-to-observation mappings. By jointly training a policy and a neural reward machine, the approach achieves zero-shot generalization comparable to methods using true grounding while outperforming existing techniques that avoid such assumptions. The work is significant for improving multi-task learning capabilities where symbolic interpretations must be inferred directly from raw sensory data like vision inputs.
Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization
arXiv:2602.09761v2 Announce Type: replace Abstract: 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 the only other previous method for multi-task learning that does not assume knowledge of the true symbol grounding.
Abstract: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 the only other previous method for multi-task learning that does not assume knowledge of the true symbol grounding.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2602.09761 [cs.LG]
(or arXiv:2602.09761v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2602.09761 Focus to learn more arXiv-issued DOI via DataCite
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