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Online Optimization Method of Learning Process for Meta-Learning.
- Source :
-
Computer Journal . May2024, Vol. 67 Issue 5, p1645-1651. 7p. - Publication Year :
- 2024
-
Abstract
- Meta-learning is a pivotal and potentially influential machine learning approach to solve challenging problems in reinforcement learning. However, the costly hyper-parameter tuning for training stability of meta-learning is a known shortcoming and currently a hotspot of research. This paper addresses this shortcoming by introducing an online and easily trainable hyper-parameter optimization approach, called Meta Parameters Learning via Meta-Learning (MPML), to combine online hyper-parameter adjustment scheme into meta-learning algorithm, which reduces the need to tune hyper-parameters. Specifically, a basic learning rate for each training task is put forward. Besides, the proposed algorithm dynamically adapts multiple basic learning rate and a shared meta-learning rate through conducting gradient descent alongside the initial optimization steps. In addition, the sensitivity with respect to hyper-parameter choices in the proposed approach are also discussed compared with model-agnostic meta-learning method. The experimental results on reinforcement learning problems demonstrate MPML algorithm is easy to implement and delivers more highly competitive performance than existing meta-learning methods on a diverse set of challenging control tasks. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00104620
- Volume :
- 67
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Computer Journal
- Publication Type :
- Academic Journal
- Accession number :
- 178019534
- Full Text :
- https://doi.org/10.1093/comjnl/bxad089