no code implementations • 5 Feb 2024 • Yixiang Shan, Zhengbang Zhu, Ting Long, Qifan Liang, Yi Chang, Weinan Zhang, Liang Yin
Applying diffusion models in reinforcement learning for long-term planning has gained much attention recently.
no code implementations • 4 Feb 2024 • Guanghe Li, Yixiang Shan, Zhengbang Zhu, Ting Long, Weinan Zhang
In offline reinforcement learning (RL), the performance of the learned policy highly depends on the quality of offline datasets.
1 code implementation • 2 Nov 2023 • Zhengbang Zhu, Hanye Zhao, Haoran He, Yichao Zhong, Shenyu Zhang, Haoquan Guo, Tingting Chen, Weinan Zhang
Diffusion models surpass previous generative models in sample quality and training stability.
1 code implementation • 27 May 2023 • Zhengbang Zhu, Minghuan Liu, Liyuan Mao, Bingyi Kang, Minkai Xu, Yong Yu, Stefano Ermon, Weinan Zhang
To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller.
no code implementations • 18 Dec 2022 • Minghuan Liu, Zhengbang Zhu, Menghui Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao
In reinforcement learning applications like robotics, agents usually need to deal with various input/output features when specified with different state/action spaces by their developers or physical restrictions.
no code implementations • 7 Nov 2022 • Zhengbang Zhu, Shenyu Zhang, Yuzheng Zhuang, Yuecheng Liu, Minghuan Liu, Liyuan Mao, Ziqin Gong, Shixiong Kai, Qiang Gu, Bin Wang, Siyuan Cheng, Xinyu Wang, Jianye Hao, Yong Yu
High-quality traffic flow generation is the core module in building simulators for autonomous driving.
no code implementations • 11 Oct 2022 • Zhengbang Zhu, Rongjun Qin, JunJie Huang, Xinyi Dai, Yang Yu, Yong Yu, Weinan Zhang
The increase in the measured performance, however, can have two possible attributions: a better understanding of user preferences, and a more proactive ability to utilize human bounded rationality to seduce user over-consumption.
2 code implementations • 4 Mar 2022 • Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jianye Hao, Yong Yu, Jun Wang
Recent progress in state-only imitation learning extends the scope of applicability of imitation learning to real-world settings by relieving the need for observing expert actions.
no code implementations • NeurIPS 2021 • Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jian Shen, Jianye Hao, Yong Yu, Jun Wang
State-only imitation learning (SOIL) enables agents to learn from massive demonstrations without explicit action or reward information.
3 code implementations • 19 Oct 2020 • Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Cowen Rivers, Zheng Tian, Daniel Palenicek, Haitham Bou Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.