Organisms in nature have evolved to exhibit flexibility in face of changes to
the environment and/or to themselves. Artificial neural networks (ANNs) have
proven useful for controlling of artificial agents acting in environments.
However, most ANN models used for reinforcement learning-type tasks have a
rigid structure that does not allow for varying input sizes. Further, they fail
catastrophically if inputs are presented in an ordering unseen during
optimization. We find that these two ANN inflexibilities can be mitigated and
their solutions are simple and highly related. For permutation invariance, no
optimized parameters can be tied to a specific index of the input elements. For
size invariance, inputs must be projected onto a common space that does not
grow with the number of projections. Based on these restrictions, we construct
a conceptually simple model that exhibit flexibility most ANNs lack. We
demonstrate the model's properties on multiple control problems, and show that
it can cope with even very rapid permutations of input indices, as well as
changes in input size. Ablation studies show that is possible to achieve these
properties with simple feedforward structures, but that it is much easier to
optimize recurrent structures.

通过限制输入的排列和尺寸，构建了一种可以灵活应对变化，具有新型 ANN 模型的结构，该模型能够在多个控制问题上表现出 ANN 所缺乏的灵活性，并且可以处理输入索引的快速排列，证明了其在很大程度上具有优越性。