这篇论文提出神经符号世界模型，能让AI系统零样本适应新任务，比传统方法泛化能力更强。
研究人员提出了一种新型神经符号世界模型，该模型在强化学习中表现优异。该模型将奖励预测与观察重建解耦，仅依赖于潜在状态中的结构化符号组件。实验表明，这种模型可以在不与环境进一步交互的情况下，零样本适应同一符号状态空间中定义的新奖励函数。与纯神经网络方法相比，该模型展现出更强的泛化能力。
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. 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.