Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments: Difference between revisions
Jump to navigation
Jump to search
| Line 1: | Line 1: | ||
= Introduction = | = Introduction = | ||
Typically, the basic goal of machine learning is to design and train a model to perform a task. In Meta-learning, the goal is to | Typically, the basic goal of machine learning is to design and 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. | 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 09:41, 12 March 2018
Introduction
Typically, the basic goal of machine learning is to design and 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.