Computational Graph Optimization with Reinforcement Learning for Resource Allocation

Abstract

This paper presents a novel approach to computational graph optimization utilizing reinforcement learning (RL). Traditional static optimization methods often fail to adequately address the dynamic and evolving resource constraints encountered in modern computing environments. We propose a system where a reinforcement learning agent learns to dynamically optimize the execution order and resource allocation of a computational graph. The agent is trained to maximize performance while respecting hardware limitations and data dependencies. This adaptive optimization strategy offers a more robust and efficient solution compared to conventional static techniques. The core of the approach lies in formulating the graph optimization problem as a Markov Decision Process (MDP) and employing an RL algorithm to learn an optimal policy. We detail the key components of the system, including the state representation, action space, reward function, and the RL algorithm used for training. The potential for this methodology to improve the performance of computationally intensive applications is demonstrated through theoretical analysis and conceptual design.