Search Results for author: Xinrun Wang

Found 8 papers, 0 papers with code

CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space

no code implementations18 May 2021 Shuxin Li, Youzhi Zhang, Xinrun Wang, Wanqi Xue, Bo An

The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e. g., Counterfactual Regret Minimization (CFR).

DO-GAN: A Double Oracle Framework for Generative Adversarial Networks

no code implementations17 Feb 2021 Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, XiaoLi Li

In this paper, we propose a new approach to train Generative Adversarial Networks (GANs) where we deploy a double-oracle framework using the generator and discriminator oracles.

Continual Learning

RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

no code implementations16 Feb 2021 Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).

Multi-agent Reinforcement Learning Starcraft +1

RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning

no code implementations1 Jan 2021 Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).

Multi-agent Reinforcement Learning Starcraft +1

Inducing Cooperation via Team Regret Minimization based Multi-Agent Deep Reinforcement Learning

no code implementations18 Nov 2019 Runsheng Yu, Zhenyu Shi, Xinrun Wang, Rundong Wang, Buhong Liu, Xinwen Hou, Hanjiang Lai, Bo An

Existing value-factorized based Multi-Agent deep Reinforce-ment Learning (MARL) approaches are well-performing invarious multi-agent cooperative environment under thecen-tralized training and decentralized execution(CTDE) scheme, where all agents are trained together by the centralized valuenetwork and each agent execute its policy independently.

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