Skip to content

nmandilaras/Reinforcement-Learning-Algorithms

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

61 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Reinforcement-Learning-Algorithms

This repository is part of my Master Thesis titled: "Design and implementation of an intelligent agent, capable of sharing resources in multicore systems, using Deep Reinforcement Learning".

Implementation of a Deep Reinforcement Learning agent that is capable to share the last-level-cache of a multi-core system, between a Latency Critical Service and a number of Best Effort applications. The agent by utilising the DQN family of algorithms, achieved to keep the SLAs violation of the critical service below 3% and in the same time succeeded even a 4x speed up for the Best Effort apps, by allocating cache ways to them when possible.

Please use this identifier to cite or link to this item: http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/17662

Dependencies

In a new conda environment execute:

pip install -r requirements.txt 

Tests

In order to execute the test provided run:

python -m unittest discover rlsuite/tests

About

Implementation of Reinforcement Learning Algorithms

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages