177 citations · 995 across the 65 of their papers we have counts for
12 papers · 1 filter
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning
Hangyu Mao, Wulong Liu, Jianye Hao +5
Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about thei…
Multi-Agent Game Abstraction via Graph Attention Neural Network
Yong Liu, Weixun Wang, Yujing Hu +3
In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process…
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Yizhen Dong, Peixin Zhang, Jingyi Wang +7
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs…
Diverse Behavior Is What Game AI Needs: Generating Varied Human-Like Playing Styles Using Evolutionary Multi-Objective Deep Reinforcement Learning
Ruimin Shen, Yan Zheng, Jianye Hao +2
this paper has been withdrawn
Independent Generative Adversarial Self-Imitation Learning in Cooperative Multiagent Systems
Xiaotian Hao, Weixun Wang, Jianye Hao +1
Many tasks in practice require the collaboration of multiple agents through reinforcement learning. In general, cooperative multiagent reinforcement learning algorithms can be clas…
MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning
Haotian Fu, Hongyao Tang, Jianye Hao +2
Most meta reinforcement learning (meta-RL) methods learn to adapt to new tasks by directly optimizing the parameters of policies over primitive action space. Such algorithms work w…
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
Weixun Wang, Tianpei Yang, Yong Liu +6
A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale…
Learning Action-Transferable Policy with Action Embedding
Yu Chen, Yingfeng Chen, Zhipeng Hu +4
Transfer learning (TL) is a promising way to improve the sample efficiency of reinforcement learning. However, how to efficiently transfer knowledge across tasks with different sta…
Spectral-based Graph Convolutional Network for Directed Graphs
Yi Ma, Jianye Hao, Yaodong Yang +3
Graph convolutional networks(GCNs) have become the most popular approaches for graph data in these days because of their powerful ability to extract features from graph. GCNs appro…
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
Weixun Wang, Tianpei Yang, Yong Liu +6
In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's se…
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Hongyao Tang, Jianye Hao, Guangyong Chen +4
Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates. Value estimation heavily depends on the stochastic…
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Haotian Fu, Hongyao Tang, Jianye Hao +3
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However,…