22 citations · 36 across the 11 of their papers we have counts for
11 papers
DanZero+: Dominating the GuanDan Game through Reinforcement Learning
Youpeng Zhao, Yudong Lu, Jian Zhao +2
The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period. Recent advancements have propelled AI pro…
DIFFER: Decomposing Individual Reward for Fair Experience Replay in Multi-Agent Reinforcement Learning
Xunhan Hu, Jian Zhao, Wengang Zhou +2
Cooperative multi-agent reinforcement learning (MARL) is a challenging task, as agents must learn complex and diverse individual strategies from a shared team reward. However, exis…
DanZero: Mastering GuanDan Game with Reinforcement Learning
Yudong Lu, Jian Zhao, Youpeng Zhao +2
Card game AI has always been a hot topic in the research of artificial intelligence. In recent years, complex card games such as Mahjong, DouDizhu and Texas Hold'em have been solve…
Multi-Target Active Object Tracking with Monte Carlo Tree Search and Target Motion Modeling
Zheng Chen, Jian Zhao, Mingyu Yang +2
In this work, we are dedicated to multi-target active object tracking (AOT), where there are multiple targets as well as multiple cameras in the environment. The goal is maximize t…
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
Mingyu Yang, Jian Zhao, Xunhan Hu +3
Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all age…
DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning
Youpeng Zhao, Jian Zhao, Xunhan Hu +2
Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games. Among these games, DouDizhu, a popular c…
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
Jian Zhao, Youpeng Zhao, Weixun Wang +5
Multi-agent reinforcement learning is difficult to be applied in practice, which is partially due to the gap between the simulated and real-world scenarios. One reason for the gap…
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
Jian Zhao, Xunhan Hu, Mingyu Yang +3
Due to the partial observability and communication constraints in many multi-agent reinforcement learning (MARL) tasks, centralized training with decentralized execution (CTDE) has…
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking
Zeyu Fang, Jian Zhao, Mingyu Yang +3
Active Multi-Object Tracking (AMOT) is a task where cameras are controlled by a centralized system to adjust their poses automatically and collaboratively so as to maximize the cov…
Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy Regularization
Jian Zhao, Yue Zhang, Xunhan Hu +5
In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards. In the absence of individual reward signals, credit assignm…
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Jian Zhao, Mingyu Yang, Youpeng Zhao +4
In cooperative multi-agent tasks, a team of agents jointly interact with an environment by taking actions, receiving a team reward and observing the next state. During the interact…