22 citations · 45 across the 11 of their papers we have counts for
11 papers
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
Yang Li, Kun Xiong, Yingping Zhang +6
This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game…
An Empirical Study on Google Research Football Multi-agent Scenarios
Yan Song, He Jiang, Zheng Tian +6
Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open…
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
Jiajun Fan, Yuzheng Zhuang, Yuecheng Liu +5
The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with population-based methods, which c…
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…
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…
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…
LIGS: Learnable Intrinsic-Reward Generation Selection for Multi-Agent Learning
David Henry Mguni, Taher Jafferjee, Jianhong Wang +7
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agent systems, coordinated exploration and behaviour is critical for agents to joint…
Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention
Yunkun Xu, Zhenyu Liu, Guifang Duan +3
Safety has become one of the main challenges of applying deep reinforcement learning to real world systems. Currently, the incorporation of external knowledge such as human oversig…
An Optimal Resource Allocator of Elastic Training for Deep Learning Jobs on Cloud
Liang Hu, Jiangcheng Zhu, Zirui Zhou +3
Cloud training platforms, such as Amazon Web Services and Huawei Cloud provide users with computational resources to train their deep learning jobs. Elastic training is a service e…
Learning to Shape Rewards using a Game of Two Partners
David Mguni, Taher Jafferjee, Jianhong Wang +9
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engi…