Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For
  Task Generalization
  While artificial neural networks (ANNs) have been widely adopted in machine
learning, researchers are increasingly obsessed by the gaps between ANNs and
biological neural networks (BNNs). In this paper, we propose a framework named
as Evolutionary Plastic Recurrent Neural Networks} (EPRNN). Inspired by BNN,
EPRNN composes Evolution Strategies, Plasticity Rules, and Recursion-based
Learning all in one meta learning framework for generalization to different
tasks. More specifically, EPRNN incorporates with nested loops for meta
learning -- an outer loop searches for optimal initial parameters of the neural
network and learning rules; an inner loop adapts to specific tasks. In the
inner loop of EPRNN, we effectively attain both long term memory and short term
memory by forging plasticity with recursion-based learning mechanisms, both of
which are believed to be responsible for memristance in BNNs. The inner-loop
setting closely simulate that of BNNs, which neither query from any gradient
oracle for optimization nor require the exact forms of learning objectives. To
evaluate the performance of EPRNN, we carry out extensive experiments in two
groups of tasks: Sequence Predicting, and Wheeled Robot Navigating. The
experiment results demonstrate the unique advantage of EPRNN compared to
state-of-the-arts based on plasticity and recursion while yielding comparably
good performance against deep learning based approaches in the tasks. The
experiment results suggest the potential of EPRNN to generalize to variety of
tasks and encourage more efforts in plasticity and recursion based learning
mechanisms.
