no code implementations • CVPR 2023 • Alexander Raistrick, Lahav Lipson, Zeyu Ma, Lingjie Mei, Mingzhe Wang, Yiming Zuo, Karhan Kayan, Hongyu Wen, Beining Han, Yihan Wang, Alejandro Newell, Hei Law, Ankit Goyal, Kaiyu Yang, Jia Deng
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world.
1 code implementation • NeurIPS 2021 • Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R. Zhang, Jimmy Ba
These changes are often spurious and unrelated to the underlying problem, such as background shifts for visual input agents.
1 code implementation • NeurIPS 2021 • Zhizhou Ren, Guangxiang Zhu, Hao Hu, Beining Han, Jianglun Chen, Chongjie Zhang
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation.
no code implementations • 22 Jun 2021 • Beining Han, Zhizhou Ren, Zuofan Wu, Yuan Zhou, Jian Peng
We study deep reinforcement learning (RL) algorithms with delayed rewards.
no code implementations • ICLR 2021 • Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
no code implementations • 28 Sep 2020 • Jianhao Wang, Zhizhou Ren, Beining Han, Jianing Ye, Chongjie Zhang
Value decomposition is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings.
1 code implementation • 24 Jul 2020 • Yihan Wang, Beining Han, Tonghan Wang, Heng Dong, Chongjie Zhang
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
no code implementations • NeurIPS 2021 • Jianhao Wang, Zhizhou Ren, Beining Han, Jianing Ye, Chongjie Zhang
Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the representational capacity of value functions.