Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments: Difference between revisions

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= Introduction =
= Introduction =


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

Revision as of 09:33, 12 March 2018

Introduction

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.

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