Search Results for author: Junjie Sheng

Found 9 papers, 3 papers with code

Can language agents be alternatives to PPO? A Preliminary Empirical Study On OpenAI Gym

1 code implementation6 Dec 2023 Junjie Sheng, Zixiao Huang, Chuyun Shen, Wenhao Li, Yun Hua, Bo Jin, Hongyuan Zha, Xiangfeng Wang

The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks?

Benchmarking Decision Making +1

Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

no code implementations8 Jun 2023 Junjie Sheng, Wenhao Li, Bo Jin, Hongyuan Zha, Jun Wang, Xiangfeng Wang

Recent methods have shown that assigning reasoning ability to agents can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods.

Multi-agent Reinforcement Learning

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

no code implementations29 Nov 2022 Haochuan Cui, Junjie Sheng, Bo Jin, Yiqiu Hu, Li Su, Lei Zhu, Wenli Zhou, Xiangfeng Wang

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences.

Cloud Computing Scheduling

Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning

no code implementations21 Nov 2022 Junjie Sheng, Lu Wang, Fangkai Yang, Bo Qiao, Hang Dong, Xiangfeng Wang, Bo Jin, Jun Wang, Si Qin, Saravan Rajmohan, QIngwei Lin, Dongmei Zhang

To address these two limitations, this paper formulates the oversubscription for cloud as a chance-constrained optimization problem and propose an effective Chance Constrained Multi-Agent Reinforcement Learning (C2MARL) method to solve this problem.

Multi-agent Reinforcement Learning reinforcement-learning +1

Obtaining Dyadic Fairness by Optimal Transport

1 code implementation9 Feb 2022 Moyi Yang, Junjie Sheng, Xiangfeng Wang, Wenyan Liu, Bo Jin, Jun Wang, Hongyuan Zha

Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning.

Fairness Link Prediction

VMAgent: Scheduling Simulator for Reinforcement Learning

2 code implementations9 Dec 2021 Junjie Sheng, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, Wenli Zhou, Yiqiu Hu, Lei Zhu, Qian Peng, Hongyuan Zha, Xiangfeng Wang

A novel simulator called VMAgent is introduced to help RL researchers better explore new methods, especially for virtual machine scheduling.

Cloud Computing reinforcement-learning +2

Dealing with Non-Stationarity in MARL via Trust-Region Decomposition

no code implementations ICLR 2022 Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Hongyuan Zha

In this paper, we introduce a novel notion, the $\delta$-measurement, to explicitly measure the non-stationarity of a policy sequence, which can be further proved to be bounded by the KL-divergence of consecutive joint policies.

Multi-agent Reinforcement Learning

Structured Diversification Emergence via Reinforced Organization Control and Hierarchical Consensus Learning

no code implementations9 Feb 2021 Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Yun Hua, Hongyuan Zha

In order to improve the efficiency of cooperation and exploration, we propose a structured diversification emergence MARL framework named {\sc{Rochico}} based on reinforced organization control and hierarchical consensus learning.

Multi-agent Reinforcement Learning

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