随机变异自构造神经网络
Self-Constructing Neural Networks Through Random Mutation
摘要：
对神经结构的研究正在人工智能领域产生许多最令人兴奋的成果。越来越明显的是，任务特定的神经结构在有效解决问题方面起着至关重要的作用。提出了一种通过随机变异学习神经网络结构的简单方法。该方法表明：1）神经架构可以在代理的生命周期内学习，2）神经架构可以在没有任何初始连接或神经元的情况下在单个生命周期内构建，以及3）架构修改能够快速适应动态和新颖的任务场景。从没有任何神经元或连接开始，这种方法构建了一个能够在多个任务上实现高性能的神经架构。这种方法的终身学习能力在没有情景重置的环境中得到了证明，即使是在不断变化的形态、肢体残疾和不断变化的任务目标的情况下学习，也不会失去运动能力。
英文摘要：
The search for neural architecture is producing many of the most exciting results in artificial intelligence. It has increasingly become apparent that task-specific neural architecture plays a crucial role for effectively solving problems. This paper presents a simple method for learning neural architecture through random mutation. This method demonstrates 1) neural architecture may be learned during the agent's lifetime, 2) neural architecture may be constructed over a single lifetime without any initial connections or neurons, and 3) architectural modifications enable rapid adaptation to dynamic and novel task scenarios. Starting without any neurons or connections, this method constructs a neural architecture capable of high-performance on several tasks. The lifelong learning capabilities of this method are demonstrated in an environment without episodic resets, even learning with constantly changing morphology, limb disablement, and changing task goals all without losing locomotion capabilities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
