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A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.
A comparison of next-fit, first-fit, and best-fit
Bays, C · 1977
Earlier work this paper cites.
NUMA (non-uniform memory access): An overview
Lameter, C · 2013
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015
Earlier work this paper cites.
More than bin packing: Dynamic resource allocation strategies in cloud data centers
Wolke, A., Tsend-Ayush, B., Pfeiffer, C., and Bichler, M · 2015
Earlier work this paper cites.
3D simulation for robot arm control with deep Q-learning
James, S. and Johns, E · 2016
Earlier work this paper cites.
A survey on virtual machine scheduling in cloud computing
Liu, L. and Qiu, Z · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., et al · 2018
Cited alongside, same era.
Protean:VM allocation service at scale
Hadary, O., Marshall, L., Menache, I., Pan, A., Greeff, E. E., Dion, D., Dorminey, S., Joshi, S., Chen, Y., Russinovich, M., et al · 2020
Later among the works it cites.
Solving mixed integer programs using neural networks
Nair, V., Bartunov, S., Gimeno, F., von Glehn, I., Lichocki, P., Lobov, I., O’Donoghue, B., Sonnerat, N., Tjandraatmadja, C., Wang, P., et al · 2020
Later among the works it cites.
Reinforcement learning in dynamic task scheduling: A review
Shyalika, C., Silva, T., and Karunananda, A · 2020
Later among the works it cites.
Towards playing full moba games with deep reinforcement learning
Ye, D., Chen, G., Zhang, W., Chen, S., Yuan, B., Liu, B., Chen, J., Liu, Z., Qiu, F., Yu, H., Yin, Y., Shi, B., Wang, L., Shi, T., Fu, Q., Yang, W., Huang, L., and Liu, W · 2020
Later among the works it cites.
Learning to schedule multi-numa virtual machines via reinforcement learning
Sheng, J., Hu, Y., Zhou, W., Zhu, L., Jin, B., Wang, J., and Wang, X · 2022
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