[1] XU Xinhe,DENG Zhili,WANG Jiao,et al.Challenging issues facing computer game research[J].CAAI Transactions on Intelligent Systems,2008,3(4):288-293.(in Chinese)徐心和,邓志立,王骄,等.机器博弈研究面临的各种挑战[J].智能系统学报,2008,3(4):288-293. [2] ZHANG Xiaochuan,TANG Yan,LIANG Ningning.A 9×9 go computer game system using temporal difference[J].CAAI Transactions on Intelligent Systems,2012,7(3):278-282.(in Chinese)张小川,唐艳,梁宁宁.采用时间差分算法的九路围棋机器博弈系统[J].智能系统学报,2012,7(3):278-282. [3] SILVER D,HUANG A,MADDISON C J,et al.Mastering the game of go with deep neural networks and tree search[J].Nature,2016,529(7587):484-489. [4] SILVER D,SCHIRTTEIESER J,SIMONYAN K,et al.Mastering the game of Go without human knowledge[J].Nature,2017,550(7676):354-359. [5] CHENG Yu,LEI Xiaofeng.Research and improvement on Alpha-Beta search algorithm in gobang[J].Computer Engineering,2012,38(17):186-188.(in Chinese)程宇,雷小锋.五子棋中Alpha-Beta搜索算法的研究与改进[J].计算机工程,2012,38(17):186-188. [6] GUO Qinqin,LI Shuqin,BAO Hua.Research on evaluation function computer game of Amazon[J].Computer Engineering and Applications,2012,48(34):50-54,87.(in Chinese)郭琴琴,李淑琴,包华.亚马逊棋机器博弈系统中评估函数的研究[J].计算机工程与应用,2012,48(34):50-54,87. [7] FU Tiaoping,ZHANG Aodi,MA Binqiang.Design and realization of the naval war game system based on computer game[J].Computer Simulation,2015,32(3):14-18.(in Chinese)傅调平,张奥狄,马滨强.机器博弈海战兵棋推演系统的设计实现[J].计算机仿真,2015,32(3):14-18. [8] OATANON S,SYNNAEVE G,URIARTE A,et al.A survey of real-time strategy game AI research and competition in StarCraft[J].IEEE Transactions on Computational Intelligence and AI in Games,2013,5(4):293-311. [9] JUSTESEN N,RISI S.Learning macromanagement in starcraft from replays using deep learning[C]//Proceedings of IEEE Conference on Computational Intelligence and Games.Washington D.C.,USA:IEEE Press,2014:124-136. [10] TANG Zhentao,ZHAO Dongbin,ZHU Yuanheng,et al.Reinforcement learning for build-order production in StarCraft II[C]//Proceedings of the 7th International Conference on Information Science and Technology.Wuhan,China:[s.n.],2017:111-124. [11] PANG Zhenjia,LIU Ruoze,MENG Zhouyu,et al.On reinforcement learning for full-length game of starcraft[EB/OL].[2019-03-10].https://www.researchgate.net/publication. [12] WU B,FU Q,LIANG J,et al.Hierarchical macro strategy model for MOBA game AI[C]//Proceedings of the 34th AAAI Conference on Artificial Intelligence.Honolulu,USA:AAAI Press,2019:157-168. [13] ZHAO Dongbin,SHAO Kun,ZHU Yuanheng,et al.Overview of deep reinforcement learning:also on the development of computer go[J].Control Theory & Applications,2016,33(6):701-717.(in Chinese)赵冬斌,邵坤,朱圆恒,等.深度强化学习综述:兼论计算机围棋的发展[J].控制理论与应用,2016,33(6):701-717. [14] TANG Zhentao,SHAO Kun,ZHAO Dongbin,et al.Advances in deep reinforcement learning:from AlphaGo to AlphaGo Zero[J].Control Theory & Applications,2017,34(12):1529-1546.(in Chinese)唐振韬,邵坤,赵冬斌,等.深度强化学习进展:从AlphaGo到AlphaGo Zero[J].控制理论与应用,2017,34(12):1529-1546. [15] MA Chengqian,XIE Wei,SUN Weijie.Research on reinforcement learning technology:a review[J].Command Control and Simulation,2018,40(6):68-72.(in Chinese)马骋乾,谢伟,孙伟杰.强化学习研究综述[J].指挥控制与仿真,2018,40(6):68-72. [16] CHURCHILL D,BURO M.Portfolio greedy search and simulation for large-scale combat in StarCraft[C]//Proceedings of IEEE Conference on Computational Intelligence and Games.Niagara Falls,USA:IEEE Press,2013:223-245. [17] WANG Che,CHEN Pan,LI Yuanda,et al.Portfolio online evolution in StarCraft[C]//Proceedings of the 20th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.Burlingame,USA:AAAI Press,2016:125-134. [18] LELIS L H S.Stratified strategy selection for unit control in real-time strategy games[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence.Melbourne,Australia:[s.n.],2017:333-342. [19] CHURCHIL L D,SAFFIDINE A,BURO M.Fast heuristic search for RTS game combat scenarios[C]//Proceedings of the 8th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.Stanford,USA:AAAI Press,2012:243-254. [20] JUSTESEN N,TILLMAN B,TOGELIUS J,et al.Script and cluster-based UCT for StarCraft[C]//Proceedings of IEEE Conference on Computational Intelligence and Games.Dortmund,Germany:IEEE Press,2014:125-136. [21] USUNIER N,SYNNAEVE G,LIN Z,et al.Episodic exploration for deep deterministic policies:an application to starcraft micromanagement tasks[C]//Proceedings of the 6th International Conference on Learning Representations.Toulon,France:[s.n.],2017:148-157. [22] SHAO Kun,ZHU Yunheng,ZHAO Dongbin,et al.StarCraft micromanagement with reinforcement learning and curriculum transfer learning[J].IEEE Transactions on Emerging Topics in Computational Intelligence,2018,3(1):73-84. [23] HU Yue,LI Juntao,LI Xi,et al.Knowledge-guided agent-tactic-aware learning for StarCraft micromanagement[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence.Stockholm,Sweden:[s.n.],2018:156-167. [24] FOOERSTER J,FARQUHAR G,AFOURAS T,et al. Counterfactual multi-agent policy gradients[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence.New Orleans,USA:AAAI Press,2018:325-334. [25] SUKHBAATAR S,ARTHUR S,FERGUS R.Learning multiagent communication with backpropagation[C]//Proceedings of the 30th Annual Conference on Neural Information Processing Systems.Barcelona,Spain:[s.n.],2016:221-223. [26] PENG Peng,YUAN Quan,WEN Ying,et al.Multiagent bidirectionally-coordinated nets for learning to play starcraft combat games[C]//Proceedings of the 6th International Conference on Learning Representations.Toulon,France:[s.n.],2017:157-169. [27] BADRINARAYANAN V,KENDAL A,CIPOLLA R.Segnet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495. |