论文标题
使用时间神经网络实施在线增强学习
Implementing Online Reinforcement Learning with Temporal Neural Networks
论文作者
论文摘要
通过仿真提出和研究了用于实施高效在线增强学习的时间神经网络（TNN）体系结构。提出的T学习系统由在线实现无监督聚类的前端TNN和实现在线增强学习的后端TNN组成。强化学习范式采用生物学上合理的新赫比亚三因素学习规则。作为一个有效的示例，通过模拟研究了卡特杆问题（平衡倒置的摆）的原型实现。
A Temporal Neural Network (TNN) architecture for implementing efficient online reinforcement learning is proposed and studied via simulation. The proposed T-learning system is composed of a frontend TNN that implements online unsupervised clustering and a backend TNN that implements online reinforcement learning. The reinforcement learning paradigm employs biologically plausible neo-Hebbian three-factor learning rules. As a working example, a prototype implementation of the cart-pole problem (balancing an inverted pendulum) is studied via simulation.
