12 citations · 60 across the 10 of their papers we have counts for
10 papers
Learning to Collaborate by Grouping: a Consensus-oriented Strategy for Multi-agent Reinforcement Learning
Jingqing Ruan, Xiaotian Hao, Dong Li +1
Multi-agent systems require effective coordination between groups and individuals to achieve common goals. However, current multi-agent reinforcement learning (MARL) methods primar…
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
Pengyi Li, Hongyao Tang, Tianpei Yang +8
Learning to collaborate is critical in Multi-Agent Reinforcement Learning (MARL). Previous works promote collaboration by maximizing the correlation of agents' behaviors, which is…
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
Xiaotian Hao, Hangyu Mao, Weixun Wang +5
The state space in Multiagent Reinforcement Learning (MARL) grows exponentially with the agent number. Such a curse of dimensionality results in poor scalability and low sample eff…
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
Hangyu Mao, Chao Wang, Xiaotian Hao +6
The MineRL competition is designed for the development of reinforcement learning and imitation learning algorithms that can efficiently leverage human demonstrations to drastically…
Towards robust and domain agnostic reinforcement learning competitions
William Hebgen Guss, Stephanie Milani, Nicholay Topin +26
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Desp…
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
Xiaotian Hao, Zhaoqing Peng, Yi Ma +12
In E-commerce, advertising is essential for merchants to reach their target users. The typical objective is to maximize the advertiser's cumulative revenue over a period of time un…
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising
Xiaotian Hao, Junqi Jin, Jianye Hao +7
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. These practical problems usually exhibit two distinc…
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…
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…
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…