Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments

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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' means exactly what you would think. Here the word 'Meta' has the precise function of introducing a layer of abstraction.

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.