aiqbench analysis
This research introduces neurosymbolic world models designed to overcome the task-dependency of standard neural approaches in reinforcement learning. By decoupling observation reconstruction from reward prediction, the method enables zero-shot adaptation to new tasks using structured symbolic components without further environment interaction. The study demonstrates that this hybrid architecture offers superior generalization compared to purely neural methods while addressing interpretability challenges inherent in latent space planning.
Towards Zero-Shot Task Transfer with Neurosymbolic World Models
arXiv:2608.17959v1 Announce Type: cross Abstract: State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the structure of the underlying environment. While expressive, these models are generally task-dependent: they learn uninterpretable latent representations that are tied to the training task and thus hard to generalize to new tasks. In this work, we present a novel world model formulation where the reward prediction only depends on a subset of structured, symbolic components of the whole latent state. Decoupling observation reconstruction and reward prediction allows us to learn world models that can adapt zero-shot, i.e. without further environment interactions, to new reward functions defined over the same symbolic state space. We discuss the main advantages and challenges of learning these neurosymbolic world models and demonstrate the strong generalisation properties of our approach over purely neural methods.
Abstract:State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the structure of the underlying environment. While expressive, these models are generally task-dependent: they learn uninterpretable latent representations that are tied to the training task and thus hard to generalize to new tasks. In this work, we present a novel world model formulation where the reward prediction only depends on a subset of structured, symbolic components of the whole latent state. Decoupling observation reconstruction and reward prediction allows us to learn world models that can adapt zero-shot, i.e. without further environment interactions, to new reward functions defined over the same symbolic state space. We discuss the main advantages and challenges of learning these neurosymbolic world models and demonstrate the strong generalisation properties of our approach over purely neural methods.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2608.17959 [cs.AI]
(or arXiv:2608.17959v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.17959 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 238 words
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