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 training a model to perform the task of designing and training a model to perform a different task.

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.