no code implementations • 17 Nov 2021 • Hangyu Mao, Chao Wang, Xiaotian Hao, Yihuan Mao, Yiming Lu, Chengjie WU, Jianye Hao, Dong Li, Pingzhong Tang
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically reduce the number of environment interactions needed to solve the complex \emph{ObtainDiamond} task with sparse rewards.
no code implementations • 29 Sep 2021 • Mingyang Liu, Chengjie WU, Qihan Liu, Yansen Jing, Jun Yang, Pingzhong Tang, Chongjie Zhang
Search algorithms have been playing a vital role in the success of superhuman AI in both perfect information and imperfect information games.
no code implementations • 7 Jun 2021 • William Hebgen Guss, Stephanie Milani, Nicholay Topin, Brandon Houghton, Sharada Mohanty, Andrew Melnik, Augustin Harter, Benoit Buschmaas, Bjarne Jaster, Christoph Berganski, Dennis Heitkamp, Marko Henning, Helge Ritter, Chengjie WU, Xiaotian Hao, Yiming Lu, Hangyu Mao, Yihuan Mao, Chao Wang, Michal Opanowicz, Anssi Kanervisto, Yanick Schraner, Christian Scheller, Xiren Zhou, Lu Liu, Daichi Nishio, Toi Tsuneda, Karolis Ramanauskas, Gabija Juceviciute
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
2 code implementations • NeurIPS 2021 • Chenghao Li, Tonghan Wang, Chengjie WU, Qianchuan Zhao, Jun Yang, Chongjie Zhang
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks.
Multi-agent Reinforcement Learning reinforcement-learning +3