Towards Zero-Shot Task Transfer with Neurosymbolic World Models
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....
| Authors | Isidoro Tamassia, Lennert De Smet, Giuseppe Marra |
|---|---|
| Primary category | cs.AI |
| Published | 18 August 2026 |
| Last updated at source | 24 August 2026 |
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