Abstract
We consider the problem of learning propositional STRIPS world models from action traces alone, using a deep learning architecture (transformers) and gradient descent. The task is cast as a supervised next token prediction problem where the tokens are the actions, and an action $a$ may follow an action sequence if the hidden effects of the previous actions do not make an action precondition of $a$ false. We show that a suitable transformer architecture can faithfully represent propositional STRIPS world models, and that the models can be learned from sets of random valid (positive) and invalid (negative) action sequences alone. A number of experiments are reported.
Abstract (translated)
我们考虑仅从动作轨迹学习命题STRIPS世界模型的问题，使用深度学习架构（如变换器）和梯度下降方法。这个问题被转化为一个监督式的下一个标记预测问题，其中的标记是动作，并且动作$a$可以跟随某个动作序列的前提条件是在之前的动作所产生的隐含效应不使动作$a$的前提条件变为假的情况下。 我们展示了一种合适的变换器架构能够忠实地表示命题STRIPS世界模型，并表明仅从随机生成的有效（正样本）和无效（负样本）的动作序列集合中就可以学习到这些模型。报告了一系列的实验结果。
URL
https://arxiv.org/abs/2509.13389
