In contrast to deep reinforcement learning agents, biological neural networks are grown through a self-organized developmental process. Here we propose a new hypernetwork approach to grow artificial neural networks based on neural cellular automata (NCA). Inspired by self-organising systems and information-theoretic approaches to developmental biology, we show that our HyperNCA method can grow neural networks capable of solving common reinforcement learning tasks. Finally, we explore how the same approach can be used to build developmental metamorphosis networks capable of transforming their weights to solve variations of the initial RL task.

翻译：与深层强化学习剂相比,生物神经网络是通过一个自我组织的发展过程发展起来的。在这里,我们提出一种新的超网络方法来发展基于神经细胞自动(NCA)的人工神经网络。在自我组织系统和信息理论方法的启发下,我们展示了我们的超超NCA方法能够发展出能够解决共同强化学习任务的神经网络。最后,我们探索了如何使用同样的方法来建设能够改变其重量的发育变形网络,以解决最初RL任务的变化。