DAI 2024 - Accepted Papers and Posters

Full Papers

  1. A Comprehensive Evaluation Framework for Multi-Agent Reinforcement Learning Zonglei Jing, Xiaojun Chang, Mingxuan Zhu, Simin Li, Aishan Liu, Xiaoqian Li, Xianglong Liu
  2. A Game Theory Reward Model for Federated Learning with Probabilistic Verification Gennaro Auricchio, Harry Clough, Christopher Ho, Kaigui Bian, Changyu Dong, Kan Yang, Jie Zhang
  3. Adaptive Command : Real-Time Policy Adjustment via Language Models in StarCraft II Weiyu Ma, Dongyu Xu, Shu Lin, Haifeng Zhang, Jun Wang
  4. CoMoU: Model-based Dynamics Estimation for Efficient Offline-to-online Reinforcement Learning Dongxiang Chen, Ying Wen
  5. InPTR: Integration Prioritized Trajectory Replay Chendie Yao, Xingxing Liang, Longfei Zhang, Jincai Huang, Jun Lei, Yulong Zhang
  6. Logarithmic Function Matters Policy Gradient Deep Reinforcement Learning Qi Liu, Jingxiang Guo, Zhongjian Qiao, Pengbin Chen, Jinxuan Zhu, Yanjie Li
  7. Looking Ahead to Avoid Being Late: Solving Hard-Constrained Traveling Salesman Problem Jingxiao Chen, Ziqin Gong, Lvda Chen, Minghuan Liu, Jun Wang, Yong Yu, Weinan Zhang
  8. Multi-agent Multi-game Entity Transformer: Towards Generalist Models in MARL Rundong Wang, Weixuan Wang, Xianhan Zeng, Liang Wang, Zhengjie Lian, Yiming Gao, Feiyu Liu, Siqin Li, Xianliang Wang, Qiang Fu, Wei Yang, Lanxiao Huang, Longtao Zheng, Zinovi Rabinovich, Bo An
  9. Multi-Agent Trajectory Prediction with Scalable Diffusion Transformer Shenyu Zhang, Shixiong Kai, Chang Chen, Yuzheng Zhuang, Zhengbang Zhu, Minghuan Liu, Weinan Zhang
  10. Opponent Modeling in Multiplayer Imperfect-Information Games Sam Ganzfried, Kevin Wang, Max Chiswick
  11. Optimal Fixed-Price Mechanism with Signaling Zhikang Fan, Weiran Shen
  12. RegFTRL: Efficient Equilibrium Learning in Two-Player Zero-Sum Games Zijian Fang, Zongkai Liu, Chao Yu
  13. Variational Stochastic Games Zhiyu Zhao, Haifeng Zhang

Posters

  1. Towards Smaller and Faster GPTs Sathya Krishnan Suresh, Shunmugapriya P
  2. SOPPU: Scalable One PEFT per User Yash Jain, Mohor Banerjee