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NVIF: Neighboring Variational Information Flow for Cooperative Large-Scale Multiagent Reinforcement Learning
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 13
作者:
Chai, Jiajun
;
Zhu, Yuanheng
;
Zhao, Dongbin
Adobe PDF(2469Kb)
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浏览/下载:58/1
  |  
提交时间:2023/11/16
Large-scale multiagent
neighboring communication
reinforcement learning (RL)
variational information flow
Online Minimax Q Network Learning for Two-Player Zero-Sum Markov Games
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 卷号: 33, 期号: 3, 页码: 1228-1241
作者:
Zhu, Yuanheng
;
Zhao, Dongbin
Adobe PDF(2838Kb)
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浏览/下载:230/4
  |  
提交时间:2022/06/10
Games
Nash equilibrium
Mathematical model
Markov processes
Convergence
Dynamic programming
Training
Deep reinforcement learning (DRL)
generalized policy iteration (GPI)
Markov game (MG)
Nash equilibrium
Q network
zero sum
Event-Triggered Communication Network With Limited-Bandwidth Constraint for Multi-Agent Reinforcement Learning
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 13
作者:
Hu, Guangzheng
;
Zhu, Yuanheng
;
Zhao, Dongbin
;
Zhao, Mengchen
;
Hao, Jianye
Adobe PDF(4187Kb)
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浏览/下载:239/6
  |  
提交时间:2022/01/27
Bandwidth
Protocols
Reinforcement learning
Task analysis
Optimization
Communication networks
Multi-agent systems
Event trigger
limited bandwidth
multi-agent communication
multi-agent reinforcement learning (MARL)
Enhanced Rolling Horizon Evolution Algorithm With Opponent Model Learning: Results for the Fighting Game AI Competition
期刊论文
IEEE TRANSACTIONS ON GAMES, 2023, 卷号: 5, 期号: 1, 页码: 5 - 15
作者:
Zhentao Tang
;
Yuanheng Zhu
;
Dongbin Zhao
;
Simon M. Lucas
Adobe PDF(7686Kb)
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浏览/下载:312/68
  |  
提交时间:2021/07/05
Rolling horizon evolution
opponent model
reinforcement learning
supervised learning
fighting game
Event-Triggered Optimal Control for Partially Unknown Constrained-Input Systems via Adaptive Dynamic Programming
期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 卷号: 64, 期号: 5, 页码: 4101-4109
作者:
Zhu, Yuanheng
;
Zhao, Dongbin
;
He, Haibo
;
Ji, Junhong
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Adobe PDF(2325Kb)
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浏览/下载:557/217
  |  
提交时间:2017/09/12
Actor-critic-identifier
Concurrent Learning
Constrained Input
Event-triggered (Et) Control
Hamilton-jacobi-bellman (Hjb) Equation
Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs
期刊论文
NEUROCOMPUTING, 2017, 卷号: 238, 期号: *, 页码: 377-386
作者:
Zhang, Qichao
;
Zhao, Dongbin
;
Zhu, Yuanheng
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Adobe PDF(1508Kb)
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浏览/下载:651/273
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提交时间:2017/05/04
Adaptive Dynamic Programming
Optimal Control
Neural Network
Fully Cooperative Games
Data-driven
Constrained Input
Event-Triggered H-infinity Control for Continuous-Time Nonlinear System via Concurrent Learning
期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 卷号: 47, 期号: 7, 页码: 1071-1081
作者:
Zhang, Qichao
;
Zhao, Dongbin
;
Zhu, Yuanheng
Adobe PDF(2937Kb)
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浏览/下载:554/243
  |  
提交时间:2017/05/04
Concurrent Learning
Event-triggered Control
H-infinity Optimal Control
Neural Networks (Nns)
Zero-sum (Zs) Game
Experience Replay for Optimal Control of Nonzero-Sum Game Systems With Unknown Dynamics
期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 卷号: 46, 期号: 3, 页码: 854-865
作者:
Zhao, Dongbin
;
Zhang, Qichao
;
Wang, Ding
;
Zhu, Yuanheng
浏览
  |  
Adobe PDF(1769Kb)
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浏览/下载:527/201
  |  
提交时间:2016/06/14
Adaptive Dynamic Programming (Adp)
Experience Replay
Nonzero-sum (Nzs) Games
Optimal Control
Unknown Dynamics
Convergence Proof of Approximate Policy Iteration for Undiscounted Optimal Control of Discrete-Time Systems
期刊论文
COGNITIVE COMPUTATION, 2015, 卷号: 7, 期号: 6, 页码: 763-771
作者:
Zhu, Yuanheng
;
Zhao, Dongbin
;
He, Haibo
;
Ji, Junhong
Adobe PDF(809Kb)
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  |  
浏览/下载:263/39
  |  
提交时间:2016/01/18
Approximate Policy Iteration
Approximation Error
Optimal Control
Fuzzy Approximator
MEC-A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems
期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 2, 页码: 346-356
作者:
Zhao, Dongbin
;
Zhu, Yuanheng
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  |  
Adobe PDF(2156Kb)
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浏览/下载:284/115
  |  
提交时间:2015/09/18
Efficient Exploration
Probably Approximately Correct (Pac)
Reinforcement Learning (Rl)
State Aggregation