SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning

no code implementations • 16 Mar 2023 • Shuhan Qi, Shuhao Zhang, Qiang Wang, Jiajia Zhang, Jing Xiao, Xuan Wang

In this paper, we propose a scalable value-decomposition exploration (SVDE) method, which includes a scalable training mechanism, intrinsic reward design, and explorative experience replay.

Multi-agent Reinforcement Learning reinforcement-learning +3

Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning

no code implementations • 11 May 2022 • Shuhan Qi, Shuhao Zhang, Xiaohan Hou, Jiajia Zhang, Xuan Wang, Jing Xiao

However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training efficiency.

reinforcement-learning Reinforcement Learning (RL) +1

D2CFR: Minimize Counterfactual Regret with Deep Dueling Neural Network

no code implementations • 26 May 2021 • Huale Li, Xuan Wang, Zengyue Guo, Jiajia Zhang, Shuhan Qi

Towards this problem, a recent method, \textit{Deep CFR} alleviates the need for abstraction and expert knowledge by applying deep neural networks directly to CFR in full games.

Onfocus Detection: Identifying Individual-Camera Eye Contact from Unconstrained Images

1 code implementation • 29 Mar 2021 • Dingwen Zhang, Bo wang, Gerong Wang, Qiang Zhang, Jiajia Zhang, Jungong Han, Zheng You

Onfocus detection aims at identifying whether the focus of the individual captured by a camera is on the camera or not.

RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning

no code implementations • 10 Sep 2020 • Huale Li, Xuan Wang, Fengwei Jia, Yi-Fan Li, Yulin Wu, Jiajia Zhang, Shuhan Qi

Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.

Decision Making reinforcement-learning +1