Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

Introduction

Typically, the basic goal of machine learning is to train a model to perform a task. In Meta-learning, the goal is to train a model to perform the task of training a model to perform a task. Hence in this case the term "Meta-Learning" has the exact meaning you would expect; the word "Meta" has the precise function of introducing a layer of abstraction.

The meta-learning task can be made more concrete by a simple example. Consider the CIFAR-100 classification task that we used for our data competition.

In this paper, a probabilistic framework for meta learning is derived then applied to tasks involving simulated robotic spiders. This framework generalizes the typical machine learning set up using Markov Decision Processes.