no code implementations • 3 Jul 2023 • Yueen Ma, Dafeng Chi, Jingjing Li, Kai Song, Yuzheng Zhuang, Irwin King
The natural language generation domain has witnessed great success thanks to Transformer models.
no code implementations • 9 May 2023 • Jiajun Fan, Yuzheng Zhuang, Yuecheng Liu, Jianye Hao, Bin Wang, Jiangcheng Zhu, Hao Wang, Shu-Tao Xia
The exploration problem is one of the main challenges in deep reinforcement learning (RL).
Ranked #1 on Atari Games on Atari-57
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 • 23 Nov 2022 • Junjie Wang, Yao Mu, Dong Li, Qichao Zhang, Dongbin Zhao, Yuzheng Zhuang, Ping Luo, Bin Wang, Jianye Hao
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 14 Nov 2022 • Amir Rasouli, Randy Goebel, Matthew E. Taylor, Iuliia Kotseruba, Soheil Alizadeh, Tianpei Yang, Montgomery Alban, Florian Shkurti, Yuzheng Zhuang, Adam Scibior, Kasra Rezaee, Animesh Garg, David Meger, Jun Luo, Liam Paull, Weinan Zhang, Xinyu Wang, Xi Chen
The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS.
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.
1 code implementation • 9 Oct 2022 • Yao Mu, Yuzheng Zhuang, Fei Ni, Bin Wang, Jianyu Chen, Jianye Hao, Ping Luo
This paper addresses such a challenge by Decomposed Mutual INformation Optimization (DOMINO) for context learning, which explicitly learns a disentangled context to maximize the mutual information between the context and historical trajectories, while minimizing the state transition prediction error.
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 • Yao Mu, Yuzheng Zhuang, Bin Wang, Guangxiang Zhu, Wulong Liu, Jianyu Chen, Ping Luo, Shengbo Li, Chongjie Zhang, Jianye Hao
Model-based reinforcement learning aims to improve the sample efficiency of policy learning by modeling the dynamics of the environment.
Model-based Reinforcement Learning reinforcement-learning +1
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.
no code implementations • 15 Mar 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Hankz Hankui Zhuo, Dong Li, Wulong Liu, Jianye Hao
To address this challenge and improve the transparency, we propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL.
no code implementations • 1 Jan 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Dong Li, Kun Shao, Wulong Liu, Hankz Hankui Zhuo, Jianye Hao
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks.
Hierarchical Reinforcement Learning reinforcement-learning +2
no code implementations • 1 Jan 2021 • Yao Mu, Yuzheng Zhuang, Bin Wang, Wulong Liu, Shengbo Eben Li, Jianye Hao
The latent dynamics model summarizes an agent’s high dimensional experiences in a compact way.
no code implementations • 19 May 2020 • Cong Fei, Bin Wang, Yuzheng Zhuang, Zongzhang Zhang, Jianye Hao, Hongbo Zhang, Xuewu Ji, Wulong Liu
Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning.
1 code implementation • ICLR 2020 • Minghuan Liu, Ming Zhou, Wei-Nan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, Yong Yu
In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions.
no code implementations • 11 May 2019 • Dong Li, Qichao Zhang, Dongbin Zhao, Yuzheng Zhuang, Bin Wang, Wulong Liu, Rasul Tutunov, Jun Wang
To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.