Fetching the paper…
Reading the bibliography…
Reinforcement learning (RL) applications, where an agent can simply learn optimal behaviors by interacting with the environment, are quickly gaining tremendous success in a wide variety of applications from controlling simple pendulums to complex data centers.
Genetic algorithms and machine learning
David E. Goldberg and John H. Holland · 1988
Earlier work this paper cites.
Evolving virtual creatures
Karl Sims · 1994
Earlier work this paper cites.
Evolutionary Algorithms in Theory and Practice
Thomas B a ¨ \ddot{a} ck · 1996
Earlier work this paper cites.
A fast and elitist multiobjective genetic algorithm: Nsga-ii
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan · 2002
Earlier work this paper cites.
Kriging is well-suited to parallelize optimization
Rodolphe Le Riche Ginsbourger, David and Laurent Carraro · 2010
Earlier work this paper cites.
Playing atari with deep reinforcement learning, 2013
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
Earlier work this paper cites.
Optimizing deep learning hyper-parameters through an evolutionary algorithm
Steven R. Young, Derek C. Rose, Thomas P. Karnowski, Seung-Hwan Lim, and Robert M. Patton · 2015
Earlier work this paper cites.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis · 2016
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Earlier work this paper cites.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Kevin Jamieson Giulia DeSalvo Afshin Rostamizadeh Li, Lisha and Ameet Talwalkar · 2016
Earlier work this paper cites.
Capes: Unsupervised storage performance tuning using neural network-based deep reinforcement learning
Yan Li, Kenneth Chang, Oceane Bel, Ethan L. Miller, and Darrell D. E. Long · 2017
Earlier work this paper cites.
Deep reinforcement learning: A brief survey
K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath · 2017
Earlier work this paper cites.
Reproducibility of benchmarked deep reinforcement learning tasks for continuous control, 2017
Riashat Islam, Peter Henderson, Maziar Gomrokchi, and Doina Precup · 2017
Cited alongside, same era.
Comparison of reinforcement learning algorithms applied to the cart-pole problem
S. Nagendra, N. Podila, R. Ugarakhod, and K. George · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning, 2017
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
RLlib: Abstractions for distributed reinforcement learning
Eric Liang, Richard Liaw, Robert Nishihara, Philipp Moritz, Roy Fox, Ken Goldberg, Joseph Gonzalez, Michael Jordan, and Ion Stoica · 2018
Cited alongside, same era.
Deephyper: Asynchronous hyperparameter search for deep neural networks
P. Balaprakash, M. Salim, T. D. Uram, V. Vishwanath, and S. M. Wild · 2018
Cited alongside, same era.
Rllib: Abstractions for distributed reinforcement learning, 2018
(technical report) a hands-on introduction to deep q-learning using openai gym in python
Ankit Choudhary · 2019
Later among the works it cites.
Deep reinforcement learning that matters, 2019
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2019
Later among the works it cites.
https://gym.openai.com/docs/ ,year=2019
(technical report) getting started with gym · 2019
Later among the works it cites.
Hyp-rl : Hyperparameter optimization by reinforcement learning, 2019
Hadi S. Jomaa, Josif Grabocka, and Lars Schmidt-Thieme · 2019
Later among the works it cites.
Efficient hyperparameter optimization in deep learning using a variable length genetic algorithm, 2020
Xueli Xiao, Ming Yan, Sunitha Basodi, Chunyan Ji, and Yi Pan · 2020
Later among the works it cites.
Smartentry: Mitigating routing update overhead with reinforcement learning for traffic engineering
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, and Ion Stoica · 2018
Cited alongside, same era.
Reinforcement Learning: An Introduction
Richard S. Sutton and Andrew G. Barto · 2018
Cited alongside, same era.
Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
Cited alongside, same era.
Stable baselines
Ashley Hill, Antonin Raffin, Maximilian Ernestus, Adam Gleave, Anssi Kanervisto, Rene Traore, Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, and Yuhuai Wu · 2018
Cited alongside, same era.
Deep Reinforcement Learning Hands-On: Apply Modern RL Methods, with Deep Q-Networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More
Maxim Lapan · 2018
Cited alongside, same era.
BOHB: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
Cited alongside, same era.
Toward self-driving processes: A deep reinforcement learning approach to control
Steven Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D. Loewen, and R. Bhushan Gopaluni · 2019
Cited alongside, same era.
Junjie Zhang, Zehua Guo, Minghao Ye, and H. Jonathan Chao · 2020
Later among the works it cites.
Do optimization methods in deep learning applications matter?, 2020
Buse Melis Ozyildirim and Mariam Kiran · 2020
Later among the works it cites.
Deep reinforcement learning based control for two-dimensional coherent combining
Bashir Mohammed, Mariam Kiran, Dan Wang, Qiang Du, and Russell Wilcox · 2020
Later among the works it cites.
Loss function search for face recognition, 2020
Xiaobo Wang, Shuo Wang, Cheng Chi, Shifeng Zhang, and Tao Mei · 2020
Later among the works it cites.
10 hyperparameter optimization frameworks
Sivasai Yadav Mudugandla · 2020
Later among the works it cites.
Provably efficient online hyperparameter optimization with population-based bandits, 2021
Jack Parker-Holder, Vu Nguyen, and Stephen Roberts · 2021
Later among the works it cites.