Applying game-learning environments to power capping scenarios via reinforcement learning



Downloads per month over past year

Hernández Aguado, Pablo and Costero Valero, Luis María and Olcoz Herrero, Katzalin and Igual Peña, Francisco Daniel (2022) Applying game-learning environments to power capping scenarios via reinforcement learning. In Cloud Computing, Big Data and Emerging Topics. Communications in Computer and Information Science, 1634 (1634). Springer international Publishing, Nueva York, pp. 91-106. ISBN 978-3-031-14598-8

[thumbnail of Olcoz29 postprint+EMB(05-ago-2023).pdf] PDF
Restringido a Repository staff only hasta 5 August 2023.


Official URL:


Research in deep learning for video game playing has received much attention and provided very relevant results in the last years. Frameworks and libraries have been developed to ease game playing research leveraging Reinforcement Learning techniques. In this paper, we propose to use two of them (RLLIB and GYM) in a very different scenario, such as learning to apply resource management policies in a multi-core server, specifically, we leverage the facilities of both frameworks coupled to derive policies for power-capping. Using RLlib and Gym enables implementing different resource management policies in a simple and fast way and, as they are based on neural networks, guarantees the efficiency in the solution, and the use of hardware accelerators for both training and inference. The results demonstrate that game-learning environments provide an effective support to cast a completely different scenario, and open new research avenues in the field of resource management using reinforcement learning techniques with minimal development effort.

Item Type:Book Section
Additional Information:

© Conference on Cloud Computing, Big Data and Emerging Topics (10. 2022. La Plata, Argentina)
ISSN 1865-0929
This work was supported by the EU (FEDER) and Spanish MINECO (RTI2018-093684-B-I00), and Comunidad de Madrid under the Multiannual Agreement with Complutense University in the line Program to Stimulate Research for Young Doctors in the context of the V PRICIT under projects PR65/19-22445 and CM S2018/TCS-4423.

Uncontrolled Keywords:Management; Reinforcement learning; RLLIB; GYM; Resource management; Power capping; DVFS
Subjects:Sciences > Computer science > Artificial intelligence
ID Code:75909
Deposited On:20 Jan 2023 18:26
Last Modified:20 Jan 2023 18:26

Origin of downloads

Repository Staff Only: item control page