313 citations · 1.8k across the 83 of their papers we have counts for
9 papers · 1 filter
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…
-Rank: Practically Scaling -Rank through Stochastic Optimisation
Yaodong Yang, Rasul Tutunov, Phu Sakulwongtana +1
Recently, -Rank, a graph-based algorithm, has been proposed as a solution to ranking joint policy profiles in large scale multi-agent systems. -Rank claimed tractability thro…
Bi-level Actor-Critic for Multi-agent Coordination
Haifeng Zhang, Weizhe Chen, Zeren Huang +4
Coordination is one of the essential problems in multi-agent systems. Typically multi-agent reinforcement learning (MARL) methods treat agents equally and the goal is to solve the…
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…
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…
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
Rui Luo, Qiang Zhang, Yaodong Yang +1
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated mode…
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Ying Wen, Yaodong Yang, Rui Luo +2
Humans are capable of attributing latent mental contents such as beliefs or intentions to others. The social skill is critical in daily life for reasoning about the potential conse…
Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning
Minne Li, Zhiwei, Qin +7
A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Tradition…
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
Ying Wen, Yaodong Yang, Rui Luo +1
Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's co…