Metatrainers

Metatrainers are a concept that has emerged in discussions surrounding artificial intelligence, particularly in the context of reinforcement learning. A metatrainer is an AI system designed to learn how to train other AI systems more effectively. Instead of directly learning a specific task, such as playing chess or recognizing images, the metatrainer's objective is to optimize the learning process of a separate AI, often referred to as the "base learner" or "agent."

The core idea behind metatraining is to accelerate and improve the efficiency of machine learning. A metatrainer might learn, for instance, what learning algorithms are best suited for particular types of problems, or how to set optimal hyperparameters for a given task. It could also learn to adapt the training environment or the reward signals provided to the base learner to promote faster convergence or better performance. Essentially, the metatrainer is learning the "art" of machine learning itself, rather than a specific application of it.

This approach draws parallels to human experts who develop new teaching methods or educational strategies. In the realm of AI, metatrainers aim to automate and generalize the process of discovering effective learning strategies, potentially leading to more robust and adaptable AI systems across a wider range of applications. Research in this area explores how metatrainers can be implemented and how they can learn generalized principles of learning that transfer to novel tasks.