Building neural networks for resource allocation for iterative workloads using reinforcement learning

Abstract

Reinforcement learning agents for resource allocation for iterative workloads, such as training Deep Neural Networks, are configured. One method comprises obtaining a specification of an iterative workload comprising multiple states and a set of available actions for each state, and a domain model of the iterative workload relating allocated resources with service metrics; adjusting weights of a reinforcement learning agent by performing iteration steps for each simulated iteration of the iterative workload and using variables from the simulated iteration to refine the reinforcement learning agent; and determining a dynamic resource allocation policy for the iterative workload. The exemplary iteration steps comprise: (a) selecting an action for a current state, obtaining a reward for the selected action and selecting a next state based on the current state and/or the selected action; (b) updating a function that evaluates a quality of a plurality of state-action combinations; and (c) repeating steps (a) and (b) with a new allocation of resources.