论文标题
放弃控制：神经元作为加强学习剂
Giving Up Control: Neurons as Reinforcement Learning Agents
论文作者
论文摘要
人工智能从历史上一直依赖于专家设计的计划，启发式方法和手工制作的方法。一直声称追求智力的创造。这种方法未能承认智力是从复杂系统中的动态中出现的。大脑中的神经元受局部规则支配，该规则没有单个神经元或一组神经元坐标或控制其他神经元。这种局部结构产生了智力可以出现的适当动态。神经元的种群必须与邻居竞争资源，抑制和活动代表。同时，它们必须合作，以便人口和生物可以执行高级功能。为此，我们将建模神经元作为加强学习剂引入。如果每个神经元都可以被视为独立演员，试图最大程度地发挥自己的自身利益。通过以这种方式进行学习，我们为建立智能系统的全新方法打开了大门。
Artificial Intelligence has historically relied on planning, heuristics, and handcrafted approaches designed by experts. All the while claiming to pursue the creation of Intelligence. This approach fails to acknowledge that intelligence emerges from the dynamics within a complex system. Neurons in the brain are governed by local rules, where no single neuron, or group of neurons, coordinates or controls the others. This local structure gives rise to the appropriate dynamics in which intelligence can emerge. Populations of neurons must compete with their neighbors for resources, inhibition, and activity representation. At the same time, they must cooperate, so the population and organism can perform high-level functions. To this end, we introduce modeling neurons as reinforcement learning agents. Where each neuron may be viewed as an independent actor, trying to maximize its own self-interest. By framing learning in this way, we open the door to an entirely new approach to building intelligent systems.
