MABSearch is an elementary/ easy to use, gradient descent based Global Optimization algorithm. It uses RL to learn the optimal learning rate for the given objective function.
MABSearch: The Bandit Way of Learning the Learning Rate - A Harmony Between Reinforcement Learning and Gradient Descent Published in: National Academy Science Letters Journal, Springer Publication [SCI Indexed]. Link to paper: https://link.springer.com/article/10.1007/s40009-023-01292-1
PDF of the full paper available at: https://rdcu.be/ddJ8n
What is Optimization (Video Explanation): https://www.youtube.com/watch?v=Gu7si5T0z_w
How to Cite: Syed Shahul Hameed, A.S., Rajagopalan, N. MABSearch: The Bandit Way of Learning the Learning Rate—A Harmony Between Reinforcement Learning and Gradient Descent. Natl. Acad. Sci. Lett. (2023). https://doi.org/10.1007/s40009-023-01292-1
How to Use: There are two ipython jupyter notebook in this repository. 0. No special prerequisite packages are required. The notebook can be downloaded and executed or the code can be simply copied.
- An experiment-ready version titled as: "MABSearch (Experiment Ready Version).ipynb". This note book has all the GD and the proposed MABSearch algroithm.
- An easier-to-understand version titled as: "MABSearch.ipynb", with comments explaining the proposed MABSearch algorithm alone.
For Any suggestions or doubt mail to: shahulshan81@gmail.com Cite the paper, if you find it useful.