Teacher–student curriculum learning

OpenAI Blog Papers

Summary

OpenAI proposes Teacher–Student Curriculum Learning (TSCL), a framework where a Teacher algorithm automatically selects subtasks for a Student to learn complex tasks, optimizing based on learning curve slope and preventing forgetting. The approach matches or surpasses hand-crafted curricula on decimal addition and Minecraft navigation tasks, enabling solutions previously impossible with direct training.

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# Teacher–student curriculum learning Source: [https://openai.com/index/teacher-student-curriculum-learning/](https://openai.com/index/teacher-student-curriculum-learning/) OpenAI## Abstract We propose Teacher–Student Curriculum Learning \(TSCL\), a framework for automatic curriculum learning, where the Student tries to learn a complex task and the Teacher automatically chooses subtasks from a given set for the Student to train on\. We describe a family of Teacher algorithms that rely on the intuition that the Student should practice more those tasks on which it makes the fastest progress, i\.e\. where the slope of the learning curve is highest\. In addition, the Teacher algorithms address the problem of forgetting by also choosing tasks where the Student's performance is getting worse\. We demonstrate that TSCL matches or surpasses the results of carefully hand\-crafted curricula in two tasks: addition of decimal numbers with LSTM and navigation in Minecraft\. Using our automatically generated curriculum enabled to solve a Minecraft maze that could not be solved at all when training directly on solving the maze, and the learning was an order of magnitude faster than uniform sampling of subtasks\.

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