Temporally Extended Actions For Reinforcement Learning Based Schedulers
Temporally Extended Actions For Reinforcement Learning Based Schedulers Prakhar Ojha, Siddhartha R Thota, Vani M and Mohit P Tahilianni National Institute of Technology Karnataka, India ABSTRACT Temporally extended actions have been proved to enhance the performance of reinforcement learning agents. The broader framework of ‘Options’ gives us a flexible way of representing such extended course of action in Markov decision processes. In this work we try to adapt options framework to model an operating system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting for its execution. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and subsequent context switch leads to considerable overhead. In this work we try to utilize the historical performances of a scheduler and try to reduce the number of redundant context switches. We propose a machine-learning module, based on temporally extended reinforcemen...