  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.
