1 code implementation • 10 Jun 2024 • Ziru Liu, Shuchang Liu, Bin Yang, Zhenghai Xue, Qingpeng Cai, Xiangyu Zhao, Zijian Zhang, Lantao Hu, Han Li, Peng Jiang
Recommender systems aim to fulfill the user's daily demands.
1 code implementation • 2 May 2024 • Safa Messaoud, Billel Mokeddem, Zhenghai Xue, Linsey Pang, Bo An, Haipeng Chen, Sanjay Chawla
We derive a closed-form expression of the entropy of such policies.
no code implementations • 26 Mar 2024 • Longtao Zheng, Zhiyuan Huang, Zhenghai Xue, Xinrun Wang, Bo An, Shuicheng Yan
General virtual agents need to handle multimodal observations, master complex action spaces, and self-improve in dynamic, open-domain environments.
no code implementations • 6 Oct 2023 • Zhenghai Xue, Qingpeng Cai, Tianyou Zuo, Bin Yang, Lantao Hu, Peng Jiang, Kun Gai, Bo An
One challenge in large-scale online recommendation systems is the constant and complicated changes in users' behavior patterns, such as interaction rates and retention tendencies.
no code implementations • 11 Aug 2023 • Yue Feng, Shuchang Liu, Zhenghai Xue, Qingpeng Cai, Lantao Hu, Peng Jiang, Kun Gai, Fei Sun
For response generation, we utilize the generation ability of LLM as a language interface to better interact with users.
no code implementations • NeurIPS 2023 • Zhenghai Xue, Qingpeng Cai, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Data with dynamics shift are separated according to their environment parameters to train the corresponding policy.
no code implementations • 3 Mar 2023 • Zhenghai Xue, Zhenghao Peng, Quanyi Li, Zhihan Liu, Bolei Zhou
Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, providing safety guarantee and exploration guidance.
1 code implementation • 3 Feb 2023 • Qingpeng Cai, Zhenghai Xue, Chi Zhang, Wanqi Xue, Shuchang Liu, Ruohan Zhan, Xueliang Wang, Tianyou Zuo, Wentao Xie, Dong Zheng, Peng Jiang, Kun Gai
One the one hand, the platforms aims at optimizing the users' cumulative watch time (main goal) in long term, which can be effectively optimized by Reinforcement Learning.
1 code implementation • 6 Dec 2022 • Wanqi Xue, Qingpeng Cai, Zhenghai Xue, Shuo Sun, Shuchang Liu, Dong Zheng, Peng Jiang, Kun Gai, Bo An
Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult.
2 code implementations • 26 Sep 2021 • Quanyi Li, Zhenghao Peng, Lan Feng, Qihang Zhang, Zhenghai Xue, Bolei Zhou
Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic.
1 code implementation • NeurIPS 2021 • Xu-Hui Liu, Zhenghai Xue, Jing-Cheng Pang, Shengyi Jiang, Feng Xu, Yang Yu
In reinforcement learning, experience replay stores past samples for further reuse.