Improving a Reinforcement Learning Algorithm for Resource Scheduling
This thesis aims to further investigate the viability of using reinforcement learning, specifically Q-learning, to schedule shared resources on the Ericsson Many-Core Architecture (EMCA). This was first explored by Patrik Trulsson in his master thesis Dynamic Scheduling of Shared Resources using Reinforcement Learning (2021). The shared resources complete jobs assigned to them, and the jobs have d