Dynamic Matrix Multiplication Using Reinforcement Learning

Abstract

Efficient matrix multiplication is a core computational task in machine learning, deep learning, and scientific computing, as it significantly impacts the performance of model training and inference. This project explores the use of reinforcement learning (RL) to dynamically select the optimal matrix multiplication algorithm based on input matrix characteristics. We compare traditional matrix multiplication methods, including Strassen’s algorithm, Winograd’s algorithm, and tiled matrix multiplication, and implement a Q- learning-based agent to choose the most efficient algorithm adaptively. Our RL-driven approach enables real-time algorithm selection, balancing computational speed and resource usage across different matrix types and sizes. Experimental results demonstrate the system's ability to reduce execution time by selecting algorithms suited to specific scenarios, providing insights into algorithm efficiency and adaptability for various data sizes. Furthermore, this research suggests practical optimization strategies for handling large-scale computations in machine learning applications, including using parallel processing, GPU acceleration, and parameter tuning to minimize computational bottlenecks. This project underscores the potential of integrating RL for adaptive computation within AI workflows, offering a scalable solution for enhancing matrix operation efficiency in data-intensive applications