中文版 | English

在结果中检索

CASIA OpenIR
（本次检索基于用户作品认领结果）

浏览/检索结果: 共6条，第1-6条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
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)  |  收藏  |  浏览/下载：220/61  |  提交时间：2021/07/05
Rolling horizon evolution  opponent model  reinforcement learning  supervised learning  fighting game  
Empirical Policy Optimization for n-Player Markov Games 期刊论文
IEEE Transactions on Cybernetics, 2022, 页码: doi={10.1109/TCYB.2022.3179775}
作者:  Yuanheng Zhu;  Weifan Li;  Mengchen Zhao;  Jianye Hao;  Dongbin Zhao
Adobe PDF(1739Kb)  |  收藏  |  浏览/下载：89/36  |  提交时间：2023/04/26
UNMAS: Multiagent Reinforcement Learning for Unshaped Cooperative Scenarios 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 12
作者:  Chai, Jiajun;  Li, Weifan;  Zhu, Yuanheng;  Zhao, Dongbin;  Ma, Zhe;  Sun, Kewu;  Ding, Jishiyu
Adobe PDF(3402Kb)  |  收藏  |  浏览/下载：230/24  |  提交时间：2022/01/27
Multi-agent systems  Training  Task analysis  Reinforcement learning  Sun  Learning systems  Semantics  Centralized training with decentralized execution (CTDE)  multiagent  reinforcement learning  StarCraft II  
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
浏览  |  Adobe PDF(1508Kb)  |  收藏  |  浏览/下载：609/265  |  提交时间：2017/05/04
Adaptive Dynamic Programming  Optimal Control  Neural Network  Fully Cooperative Games  Data-driven  Constrained Input  
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
浏览  |  Adobe PDF(2156Kb)  |  收藏  |  浏览/下载：258/106  |  提交时间：2015/09/18
Efficient Exploration  Probably Approximately Correct (Pac)  Reinforcement Learning (Rl)  State Aggregation  
Full-range adaptive cruise control based on supervised adaptive dynamic programming 期刊论文
NEUROCOMPUTING, 2014, 卷号: 125, 页码: 57-67
作者:  Zhao, Dongbin;  Hu, Zhaohui;  Xia, Zhongpu;  Alippi, Cesare;  Zhu, Yuanheng;  Wang, Ding
浏览  |  Adobe PDF(2228Kb)  |  收藏  |  浏览/下载：391/114  |  提交时间：2015/08/12
Adaptive Dynamic Programming  Supervised Reinforcement Learning  Neural Networks  Adaptive Cruise Control  Stop And Go