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Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI
Lei Han · Peng Sun · Yali Du · Jiechao Xiong · Qing Wang · Xinghai Sun · Han Liu · Tong Zhang

Tue Jun 11 06:30 PM -- 09:00 PM (PDT) @ Pacific Ballroom #243

We consider the problem of multi-agent reinforcement learning (MARL) in video game AI, where the agents are located in a spatial grid-world environment and the number of agents varies both within and across episodes. The challenge is to flexibly control an arbitrary number of agents while achieving effective collaboration. Existing MARL methods usually suffer from the trade-off between these two considerations. To address the issue, we propose a novel architecture that learns a spatial joint representation of all the agents and outputs grid-wise actions. Each agent will be controlled independently by taking the action from the grid it occupies. By viewing the state information as a grid feature map, we employ a convolutional encoder-decoder as the policy network. This architecture naturally promotes agent communication because of the large receptive field provided by the stacked convolutional layers. Moreover, the spatially shared convolutional parameters enable fast parallel exploration that the experiences discovered by one agent can be immediately transferred to others. The proposed method can be conveniently integrated with general reinforcement learning algorithms, e.g., PPO and Q-learning. We demonstrate the effectiveness of the proposed method in extensive challenging multi-agent tasks in StarCraft II.

Author Information

Lei Han (Tencent AI Lab)
Peng Sun (Tencent AI Lab)
Yali Du (University of Technology Sydney)

Yali Du is a 3rd year PhD student with her research focusing on matrix completion and its applications on recommender systems, multi-label learning and social analysis. She has the enthusiasm to communicate with other researchers and learn from them. She has published two full-length papers on IJCAI 2017.

Jiechao Xiong (Tencent AI Lab)
Qing Wang (Tencent AI Lab)
Xinghai Sun (Tencent AI Lab)
Han Liu (Northwestern)
Tong Zhang (Tecent AI Lab)
Tong Zhang

Tong Zhang is a professor of Computer Science and Mathematics at the Hong Kong University of Science and Technology. His research interests are machine learning, big data and their applications. He obtained a BA in Mathematics and Computer Science from Cornell University, and a PhD in Computer Science from Stanford University. Before joining HKUST, Tong Zhang was a professor at Rutgers University, and worked previously at IBM, Yahoo as research scientists, Baidu as the director of Big Data Lab, and Tencent as the founding director of AI Lab. Tong Zhang was an ASA fellow and IMS fellow, and has served as the chair or area-chair in major machine learning conferences such as NIPS, ICML, and COLT, and has served as associate editors in top machine learning journals such as PAMI, JMLR, and Machine Learning Journal.

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