Dynamic Scheduling of Shared Resources using Reinforcement Learning
The goal of the thesis is to simulate the Ericsson Many-Core Architecture, EMCA, and implement a dynamic scheduler for the system using reinforcement learning methods. The system contains shared resources that receive and complete jobs. Also, the deadlines and latency definitions can change depending on the job type. The scheduler should aim to avoid missing deadlines as well as aim to reduce the
