“Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Ye Et Al 2020

“Towards Playing Full MOBA Games with Deep Reinforcement Learning”⁠, Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu2020-11-25 (RNN⁠, knowledge distillation⁠, MARL; similar; bibliography)

“Mastering Complex Control in MOBA Games With Deep Reinforcement Learning”, Ye Et Al 2019

“Mastering Complex Control in MOBA Games with Deep Reinforcement Learning”⁠, Deheng Ye, Zhao Liu, Mingfei Sun, Bei Shi, Peilin Zhao, Hao Wu, Hongsheng Yu, Shaojie Yang, Xipeng Wu2019-12-20 (Transformer NN; similar)

“OpenAI Five: 2016–2019”, OpenAI 2019

“OpenAI Five: 2016–2019”⁠, OpenAI2019-12-13 (backlinks)

“Dota 2 With Large Scale Deep Reinforcement Learning”, Berner Et Al 2019

“Dota 2 with Large Scale Deep Reinforcement Learning”⁠, Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi2019-12-13 (similar)

“Solving Rubik’s Cube With a Robot Hand”, OpenAI Et Al 2019

“Solving Rubik’s Cube with a Robot Hand”⁠, OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino2019-10-16 (meta-learning⁠, robotics⁠, RL scaling; similar)

“Solving Rubik’s Cube With a Robot Hand [blog]”, OpenAI 2019

“Solving Rubik’s Cube with a Robot Hand [blog]”⁠, OpenAI2019-10-15 (meta-learning⁠, robotics; backlinks; similar)

“How AI Training Scales”, McCandlish Et Al 2018

“How AI Training Scales”⁠, Sam McCandlish, Jared Kaplan, Dario Amodei2018-12-14 (GAN⁠, autoencoder NN⁠, AI scaling⁠, RL scaling; backlinks; similar; bibliography)

“An Empirical Model of Large-Batch Training”, McCandlish Et Al 2018

“An Empirical Model of Large-Batch Training”⁠, Sam McCandlish, Jared Kaplan, Dario Amodei, Open A. I. Dota Team2018-12-14 (autoencoder NN⁠, AI scaling/hardware⁠, RL scaling; similar)

“Emergent Complexity via Multi-Agent Competition”, Bansal Et Al 2017

“Emergent Complexity via Multi-Agent Competition”⁠, Trapit Bansal, Jakub Pachocki, Szymon Sidor, Ilya Sutskever, Igor Mordatch2017-10-10 (RL exploration⁠, MARL; similar)

“Proximal Policy Optimization Algorithms”, Schulman Et Al 2017

“Proximal Policy Optimization Algorithms”⁠, John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov2017-07-20 (robotics; similar)

“Net2Net: Accelerating Learning via Knowledge Transfer”, Chen Et Al 2015

“Net2Net: Accelerating Learning via Knowledge Transfer”⁠, Tianqi Chen, Ian Goodfellow, Jonathon Shlens2015-11-18 (knowledge distillation⁠, AI scaling; backlinks; similar)

“If You Want to Solve a Hard Problem in Reinforcement Learning, You Just Scale. It’s Just Gonna Work Just like Supervised Learning. It’s the Same, the Same Story Exactly. It Was Kind of Hard to Believe That Supervised Learning Can Do All Those Things, but It’s Not Just Vision, It’s Everything and the Same Thing Seems to Hold for Reinforcement Learning Provided You Have a Lot of Experience.”

Wikipedia

1. Dota 2

2. OpenAI Five