论文标题
HyperNCA：具有神经细胞自动机的发展发展网络
HyperNCA: Growing Developmental Networks with Neural Cellular Automata
论文作者
论文摘要
与深度强化学习剂相反，生物神经网络是通过自组织的发展过程而发展的。在这里，我们提出了一种基于神经细胞自动机（NCA）的人工神经网络的新型超网络方法。受到自组织的系统和信息理论生物学方法的启发，我们表明我们的HyperNCA方法可以发展能够解决共同强化学习任务的神经网络。最后，我们探讨了如何使用相同的方法来构建能够转换其权重以解决初始RL任务变化的发育变态网络。
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.
