多智能体深度强化学习的若干关键科学问题

孙长银; 穆朝絮

doi:10.16383/j.aas.c200159

多智能体深度强化学习的若干关键科学问题

doi: 10.16383/j.aas.c200159

孙长银^1,,
穆朝絮^2,

1.
东南大学自动化学院南京 210096
2.
天津大学电气自动化与信息工程学院天津 300072

基金项目: 科技部人工智能专项重大项目 (2018AAA0101400), 国家自然科学基金创新研究群体(61921004), 国家自然科学基金(61942301)资助

详细信息

作者简介:
孙长银：东南大学自动化学院教授. 主要研究方向为智能控制与优化, 强化学习, 神经网络, 数据驱动控制. 本文通信作者.E-mail: cysun@seu.edu.cn

穆朝絮：天津大学电气自动化与信息工程学院教授. 主要研究方向为强化学习, 自适应学习系统, 非线性控制和优化.E-mail: cxmu@tju.edu.cn

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出版历程
- 收稿日期: 2020-03-25
- 网络出版日期: 2020-07-24
- 刊出日期: 2020-07-24

Important Scientific Problems of Multi-Agent Deep Reinforcement Learning

SUN Chang-Yin^1
,,
MU Chao-Xu^2
,

1.
School of Automation, Southeast University, Nanjing 210096
2.
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072

Funds: Supported by Artificial Intelligence Major Project of the Ministry of Science and Technology of China (2018AAA0101400), National Natural Science Foundation of China for Creative Research Groups (61921004), National Natural Science Foundation of China (61942301)

摘要

摘要: 强化学习作为一种用于解决无模型序列决策问题的方法已经有数十年的历史, 但强化学习方法在处理高维变量问题时常常会面临巨大挑战. 近年来, 深度学习迅猛发展, 使得强化学习方法为复杂高维的多智能体系统提供优化的决策策略、在充满挑战的环境中高效执行目标任务成为可能. 本文综述了强化学习和深度强化学习方法的原理, 提出学习系统的闭环控制框架, 分析了多智能体深度强化学习中存在的若干重要问题和解决方法, 包括多智能体强化学习的算法结构、环境非静态和部分可观性等问题, 对所调查方法的优缺点和相关应用进行分析和讨论. 最后提供多智能体深度强化学习未来的研究方向, 为开发更强大、更易应用的多智能体强化学习控制系统提供一些思路.
- 强化学习 /
- 深度强化学习 /
- 多智能体 /
- 学习系统 /
- 智能控制 /
- 决策优化
Abstract: Reinforcement learning has been used to solve sequence decision problems without models for decades. However, it often faces great challenges in dealing with high-dimensional problems. In recent years, with the rapid development of deep learning, it promotes that reinforcement learning can provide the optimized strategy for complex and high-dimensional multi-agent systems to efficiently perform the target tasks in challenging environments. This paper reviews on the principles of reinforcement learning and deep reinforcement learning, puts forward the closed-loop control framework of learning systems, and investigates the existing important problems and corresponding methods for the deep reinforcement learning of multi-agent systems, including multi-agent reinforcement learning algorithmic framework, non-static environment, partially observability, and so on. The merits and drawbacks of these investigated methods are analyzed, and some related applications are summarized. This paper also provides some new insights into various research directions of multi-agent reinforcement learning, and related ideas for better application development in the future.
- Reinforcement learning /
- deep reinforcement learning /
- multi-agent /
- learning system /
- intelligent control /
- decision optimization

HTML全文

图 1 强化学习的基本原理

Fig. 1 Basic principles of reinforcement learning

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图 2 深度强化学习原理图

Fig. 2 Schematic diagram of deep reinforcement learning

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图 3 学习系统闭环控制框架

Fig. 3 Relearnware: closed-loop control framework of learning systems

下载: 全尺寸图片幻灯片

参考文献(95)

[1]	Rubenstein M, Cornejo A, Nagpal R. Programmable self-assembly in a thousand-robot swarm. Science, 2014, 345(6198): 795−799 doi: 10.1126/science.1254295
[2]	Wang Y D, He H B, Sun C Y. Learning to navigate through complex dynamic environment with modular deep reinforcement learning. IEEE Transactions on Games, 2018, 10(4): 400−412 doi: 10.1109/TG.2018.2849942
[3]	郑南宁. 人工智能面临的挑战. 自动化学报, 2016, 42(5): 641−642 Zheng Nan-Ning. On challenges in artificial intelligence. Acta Automatica Sinica, 2016, 42(5): 641−642
[4]	Nguyen T T, Nguyen N D, Nahavandi S. Deep reinforcement learning for multiagent systems: a review of challenges, solutions, and applications. IEEE Transactions on Cybernetics, 2020 doi: 10.1109/TCYB.2020.2977374
[5]	赵冬斌, 邵坤, 朱圆恒, 李栋, 陈亚冉, 王海涛, 等. 深度强化学习综述: 兼论计算机围棋的发展. 控制理论与应用, 2016, 33(6): 701−717 doi: 10.7641/CTA.2016.60173 Zhao Dong-Bin, Shao Kun, Zhu Yuan-Heng, Li Dong, Chen Ya-Ran, Wang Hai-Tao, et al. Review of deep reinforcement learning and discussions on the development of computer Go. Control Theory & Applications, 2016, 33(6): 701−717 doi: 10.7641/CTA.2016.60173
[6]	周志华. AlphaGo专题介绍. 自动化学报, 2016, 42(5): 670 Zhou Zhi-Hua. AlphaGo special session: an introduction. Acta Automatica Sinica, 2016, 42(5): 670
[7]	Silver D, Huang A, Maddison C J, Guez A, Sifre L, van den Driessche G, et al. Mastering the game of go with deep neural networks and tree search. Nature, 2016, 529(7587): 484−489 doi: 10.1038/nature16961
[8]	Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of go without human knowledge. Nature, 2017, 550(7676): 354−359 doi: 10.1038/nature24270
[9]	Berner C, Brockman G, Chan B, Cheung V, Dębiak P, Denniso C, et al. Dota 2 with large scale deep reinforcement learning. arXiv: 1912.06680, 2019.
[10]	Hung S M, Givigi S N. A Q-learning approach to flocking with UAVs in a stochastic environment. IEEE Transactions on Cybernetics, 2017, 47(1): 186−197 doi: 10.1109/TCYB.2015.2509646
[11]	Schwab D, Zhu Y F, Veloso M. Zero shot transfer learning for robot soccer. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018). Stockholm, Sweden: ACM, 2018. 2070−2072
[12]	王云鹏, 郭戈. 基于深度强化学习的有轨电车信号优先控制. 自动化学报, 2019, 45(12): 2366−2377 Wang Yun-Peng, Guo Ge. Signal priority control for trams using deep reinforcement learning. Acta Automatica Sinica, 2019, 45(12): 2366−2377
[13]	Rahman M S, Mahmud M A, Pota H R, Hossain M J, Orchi T F. Distributed multi-agent-based protection scheme for transient stability enhancement in power systems. International Journal of Emerging Electric Power Systems, 2015, 16(2): 117−129 doi: 10.1515/ijeeps-2014-0143
[14]	He J, Peng J, Jiang F, Qin G R, Liu W R. A distributed Q learning spectrum decision scheme for cognitive radio sensor network. International Journal of Distributed Sensor Networks, 2015, 2015: 7
[15]	Leibo J Z, Zambaldi V, Lanctot M, Marecki J, Graepel T. Multi-agent reinforcement learning in sequential social dilemmas. In: Proceedings of the 16th Conference on Autonomous Agents and Multiagent Systems. Sao Paulo, Brazil: ACM, 2017. 464−473
[16]	吴国政. 从F03项目资助情况分析我国自动化学科的发展现状与趋势. 自动化学报, 2019, 45(9): 1611−1619 Wu Guo-Zheng. Analysis of the status and trend of the development of China's automation discipline from F03 funding of NSFC. Acta Automatica Sinica, 2019, 45(9): 1611−1619
[17]	Hernandez-Leal P, Kartal B, Taylor M E. A survey and critique of multiagent deep reinforcement learning. Autonomous Agents and Multi-Agent Systems, 2019, 33(6): 750−797 doi: 10.1007/s10458-019-09421-1
[18]	Mu C X, Ni Z, Sun C Y, He H B. Air-breathing hypersonic vehicle tracking control based on adaptive dynamic programming. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(3): 584−598 doi: 10.1109/TNNLS.2016.2516948
[19]	Mu C, Zhao Q, Sun C, Gao Z. A novel Q-learning algorithm for optimal tracking control of linear discrete-time systems with unknown dynamics. Applied Soft Computing, 2019, 82: 1−13
[20]	Wang Y D, Sun J, He H B, Sun C Y. Deterministic policy gradient with integral compensator for robust quadrotor control. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019 doi: 10.1109/TSMC.2018.2884725
[21]	Sutton R S, McAllester D, Singh S, Mansour Y. Policy gradient methods for reinforcement learning with function approximation. In: Proceedings of the 12th International Conference on Neural Information Processing Systems. Denver, USA: MIT Press, 1999. 1057−1063
[22]	Silver D, Lever G, Heess N, Degris T, Wierstra D, Riedmiller M. Deterministic policy gradient algorithms. In: Proceedings of the 31st International Conference on Machine Learning. Beijing, China: ACM, 2014. 387−395
[23]	Wei Q L, Wang L X, Liu Y, Polycarpou M M. Optimal elevator group control via deep asynchronous actor-critic learning. IEEE Transactions on Neural Networks and Learning Systems, 2020 doi: 10.1109/TNNLS.2020.2965208
[24]	Dong L, Zhong X N, Sun C Y, He H B. Adaptive event-triggered control based on heuristic dynamic programming for nonlinear discrete-time systems. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(7): 1594−1605 doi: 10.1109/TNNLS.2016.2541020
[25]	Arulkumaran K, Deisenroth M P, Brundage M, Bharath A A. Deep reinforcement learning: a brief survey. IEEE Signal Processing Magazine, 2017, 34(6): 26−38 doi: 10.1109/MSP.2017.2743240
[26]	Li Y X. Deep reinforcement learning: an overview. arXiv: 1701.07274, 2017.
[27]	Nguyen N D, Nguyen T, Nahavandi S. System design perspective for human-level agents using deep reinforcement learning: a survey. IEEE Access, 2017, 5: 27091−27102 doi: 10.1109/ACCESS.2017.2777827
[28]	Nguyen T T. A multi-objective deep reinforcement learning framework. arXiv: 1803.02965, 2018.
[29]	Tsitsiklis J N, van Roy B. Analysis of temporal-difference learning with function approximation. In: Proceedings of the 9th International Conference on Neural Information Processing Systems. Denver, USA: MIT Press, 1996. 1075−1081
[30]	Van Hasselt H. Double Q-learning. In: Proceedings of the 23rd International Conference on Neural Information Processing Systems. Vancouver, Canada: MIT Press, 2010. 2613−2621
[31]	Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double Q-learning. arXiv: 1509.06461, 2015.
[32]	Schaul T, Quan J, Antonoglou I, Silver D. Prioritized experience replay. arXiv: 1511.05952, 2015.
[33]	Wang Z Y, Schaul T, Hessel M, van Hasselt H, Lanctot M, de Freitas N. Dueling network architectures for deep reinforcement learning. In: Proceedings of the 33rd International Conference on Machine Learning. New York, USA: ACM, 2016. 1995−2003
[34]	Hausknecht H, Stone P. Deep recurrent Q-learning for partially observable MDPs. arXiv: 1507.06527, 2017.
[35]	Lample G, Chaplot D S. Playing FPS games with deep reinforcement learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, USA: AIAA, 2017.
[36]	Sorokin I, Seleznev A, Pavlov M, Fedorov A, Ignateva A. Deep attention recurrent Q-network. arXiv: 1512.01693, 2015.
[37]	Lillicrap T P, Hunt J J, Pritzel A, Heess N, Erez T, Tassa Y, et al. Continuous control with deep reinforcement learning. arXiv: 1509.02971, 2015.
[38]	Mnih V, Badia A P, Mirza M, Graves A, Harley T, Lillicrap T P, et al. Asynchronous methods for deep reinforcement learning. In: Proceedings of the 33rd International Conference on Machine Learning. New York, USA: ACM, 2016. 1928−1937
[39]	Haarnoja T, Zhou A, Abbeel P, Levine S. Soft actor-critic: off-policy maximum entropy deep reinforcement learning with a stochastic actor. arXiv: 1801.01290, 2018.
[40]	Schulman J, Levine S, Abbeel P, Jordan M I, Moritz P. Trust region policy optimization. In: Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ACM, 2015. 1889−1897
[41]	Jadid O A, Hajinezhad D. A review of cooperative multi-agent deep reinforcement learning. arXiv: 1908.03963, 2019.
[42]	Tan M. Multi-agent reinforcement learning: independent vs. cooperative agents. In: Proceedings of the 10th International Conference on Machine Learning. Amherst, USA: ACM, 1993. 330−337
[43]	Matignon L, Laurent G J, Le Fort-Piat N. Independent reinforcement learners in cooperative markov games: a survey regarding coordination problems. The Knowledge Engineering Review, 2012, 27(1): 1−31 doi: 10.1017/S0269888912000057
[44]	Tampuu A, Matiisen T, Kodelja D, Kuzovkin I, Korjus K, Aru J, et al. Multiagent cooperation and competition with deep reinforcement learning. arXiv: 1511.08779, 2015.
[45]	Usunier N, Synnaeve G, Lin Z M, Chintala S. Episodic exploration for deep deterministic policies: an application to starcraft micromanagement tasks. arXiv: 1609.02993, 2016.
[46]	Cui L L, Wang X W, Zhang Y. Reinforcement learning-based asymptotic cooperative tracking of a class multi-agent dynamic systems using neural networks. Neurocomputing, 2016, 171: 220−229 doi: 10.1016/j.neucom.2015.06.066
[47]	Kraemer L, Banerjee B. Multi-agent reinforcement learning as a rehearsal for decentralized planning. Neurocomputing, 2016, 190: 82−94 doi: 10.1016/j.neucom.2016.01.031
[48]	Lowe R, Wu Y, Tamar A, Harb J, Abbeel P, Mordatch I. Multi-agent actor-critic for mixed cooperative-competitive environments. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: MIT Press, 2017. 6379−6390
[49]	Ryu H, Shin H, Park J. Multi-agent actor-critic with generative cooperative policy network. arXiv: 1810.09206, 2018.
[50]	Chu X X, Ye H J. Parameter sharing deep deterministic policy gradient for cooperative multi-agent reinforcement learning. arXiv: 1710.00336, 2017.
[51]	Foerster J N, Farquhar G, Afouras T, Nardelli N, Whiteson S. Counterfactual multi-agent policy gradients. arXiv: 1705.08926, 2017.
[52]	Zhang K Q, Yang Z R, Liu H, Zhang T, Basar T. Fully decentralized multi-agent reinforcement learning with networked agents. In: Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden: ACM, 2018. 5872−5881
[53]	Jiang J C, Dun C, Huang T J, Lu Z Q. Graph convolutional reinforcement learning. arXiv: 1810.09202, 2018.
[54]	Wang Q L, Psillakis H E, Sun C Y. Cooperative control of multiple agents with unknown high-frequency gain signs under unbalanced and switching topologies. IEEE Transactions on Automatic Control, 2019, 64(6): 2495−2501 doi: 10.1109/TAC.2018.2867161
[55]	Hernandez-Leal P, Kaisers M, Baarslag T, de Cote E M. A survey of learning in multiagent environments: dealing with non-stationarity. arXiv: 1707.09183, 2017.
[56]	Mnih V, Kavukcuoglu K, Silver D, Rusu A A, Veness J, Bellemare M G, et al. Human-level control through deep reinforcement learning. Nature, 2015, 518(7540): 529−533 doi: 10.1038/nature14236
[57]	Abdallah S, Kaisers M. Addressing the policy-bias of Q-learning by repeating updates. In: Proceedings of the 12th International Conference on Autonomous Agents and Multi-agent Systems. Saint Paul, USA: ACM, 2013. 1045−1052
[58]	Abdallah S, Kaisers M. Addressing environment non-stationarity by repeating Q-learning updates. The Journal of Machine Learning Research, 2016, 17(1): 1582−1612
[59]	Yu C, Zhang M J, Ren F H, Tan G Z. Emotional multiagent reinforcement learning in spatial social dilemmas. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(12): 3083−3096 doi: 10.1109/TNNLS.2015.2403394
[60]	Diallo E A O, Sugiyama A, Sugawara T. Learning to coordinate with deep reinforcement learning in doubles pong game. In: Proceedings of the 16th IEEE International Conference on Machine Learning and Applications. Cancun, Mexico: IEEE, 2017. 14−19
[61]	Foerster J N, Nardelli N, Farquhar G, Afouras T, Torr P H S, Kohli P. Stabilising experience replay for deep multi-agent reinforcement learning. In: Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: ACM, 2017. 1146−1155
[62]	Palmer G, Tuyls K, Bloembergen D, Savani R. Lenient multi-agent deep reinforcement learning. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. Stockholm, Sweden: ACM, 2018. 443−451
[63]	Omidshafiei S, Pazis J, Amato C, How J P, Vian J. Deep decentralized multi-task multi-agent reinforcement learning under partial observability. In: Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: ACM, 2017. 2681−2690
[64]	Zheng Y, Meng Z P, Hao J Y, Zhang Z Z. Weighted double deep multiagent reinforcement learning in stochastic cooperative environments. In: Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence. Nanjing, China: ACM, 2018. 421−429
[65]	Mu C X, Zhao Q, Sun C Y. Optimal model-free output synchronization of heterogeneous multi-agent systems under switching topologies. IEEE Transactions on Industrial Electronics, 2019 doi: 10.1109/TIE.2019.2958277
[66]	Foerster J N, Assael Y M, de Freitas N, Whiteson S. Learning to communicate to solve riddles with deep distributed recurrent Q-networks. arXiv: 1602.02672, 2016.
[67]	Hong Z W, Su S Y, Shann T Y, Chang Y H, Lee C Y. A deep policy inference Q-network for multi-agent systems. In: Proceedings of the 17th Conference on Autonomous Agents and Multiagent Systems. Stockholm, Sweden: Springer, 2018. 1388−1396
[68]	Kasai T, Tenmoto H, Kamiya A. Learning of communication codes in multi-agent reinforcement learning problem. In: Proceedings of 2008 IEEE Conference on Soft Computing in Industrial Applications. Muroran, Japan: IEEE, 2008. 1−6
[69]	Foerster J N, Assael Y M, de Freitas N, Whiteson S. Learning to communicate with deep multi-agent reinforcement learning. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain: ACM, 2016. 2137−2145
[70]	Sukhbaatar S, Szlam A, Fergus R. Learning multiagent communication with backpropagation. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona, Spain: ACM, 2016. 2252−2260
[71]	Zhang H G, Jiang H, Luo Y H, Xiao G Y. Data-driven optimal consensus control for discrete-time multi-agent systems with unknown dynamics using reinforcement learning method. IEEE Transactions on Industrial Electronics, 2017, 64(5): 4091−4100 doi: 10.1109/TIE.2016.2542134
[72]	Zhang Y, Zavlanos M M. Distributed off-policy actor-critic reinforcement learning with policy consensus. arXiv: 1903.09255, 2019.
[73]	Wei Q L, Liu D R, Lewis F L, Liu Y, Zhang J. Mixed iterative adaptive dynamic programming for optimal battery energy control in smart residential microgrids. IEEE Transactions on Industrial Electronics, 2017, 64(5): 4110−4120 doi: 10.1109/TIE.2017.2650872
[74]	Yang X D, Wang Y D, He H B, Sun C Y, Zhang Y B. Deep reinforcement learning for economic energy scheduling in data center microgrids. In: Proceedings of the 2019 IEEE Power & Energy Society General Meeting. Atlanta, USA: IEEE, 2019. 1−5
[75]	Prasad A, Dusparic I. Multi-agent deep reinforcement learning for zero energy communities. arXiv: 1810.03679, 2018.
[76]	徐昕. 增强学习与近似动态规划. 北京: 科学出版社, 2010 Xu Xin. Reinforcement Learning and Approximate Dynamic Programming. Beijing: Science Press, 2010
[77]	Wan Z Q, Jiang C, Fahad M, Ni Z, Guo Y, He H B. Robot-assisted pedestrian regulation based on deep reinforcement learning. IEEE Transactions on Cybernetics, 2020, 50(4): 1669−1682 doi: 10.1109/TCYB.2018.2878977
[78]	Lin K X, Zhao R Y, Xu Z, Zhou J Y. Efficient large-scale fleet management via multi-agent deep reinforcement learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London, UK: ACM, 2018. 1774−1783
[79]	Ben Noureddine D, Gharbi A, Ben Ahmed S. Multi-agent deep reinforcement learning for task allocation in dynamic environment. In: Proceedings of the 12th International Conference on Software Technologies. Madrid, Spain: SciTePress, 2017. 17−26
[80]	Hüttenrauch M, Šošić A, Neumann G. Guided deep reinforcement learning for swarm systems. arXiv: 1709.06011, 2017.
[81]	Kurek M, Jaśkowski W. Heterogeneous team deep Q-learning in low-dimensional multi-agent environments. In: Proceedings of the 2016 IEEE Conference on Computational Intelligence and Games (CIG). Santorini, Greece: IEEE, 2016. 1−8
[82]	Perolat J, Leibo J Z, Zambaldi V, Beattie C, Tuyls K, Graepel T. A multi-agent reinforcement learning model of common-pool resource appropriation. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: ACM, 2017. 3643−3652
[83]	Piot B, Geist M, Pietquin O. Bridging the gap between imitation learning and inverse reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(8): 1814−1826 doi: 10.1109/TNNLS.2016.2543000
[84]	Hadfield-Menell D, Russell S J, Abbeel P, Dragan A. Cooperative inverse reinforcement learning. In: Proceedings of the 30th Conference on Neural Information Processing Systems. Barcelona, Spain: ACM, 2016. 3909−3917
[85]	Hadfield-Menell D, Milli S, Abbeel P, Russell S, Dragan A D. Inverse reward design. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: ACM, 2017. 6765−6774
[86]	Levine S, Finn C, Darrell T, Abbeel P. End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research, 2016, 17(1): 1334−1373
[87]	Nagabandi A, Kahn G, Fearing R S, Levine S. Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning. In: Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, Australia: IEEE, 2018. 7559−7566
[88]	Gu S X, Lillicrap T P, Sutskever I, Levine S. Continuous deep Q-learning with model-based acceleration. In: Proceedings of the 33rd International Conference on Machine Learning. New York, USA: ACM, 2016. 2829−2838
[89]	Finn C, Levine S. Deep visual foresight for planning robot motion. In: Proceedings of the 2017 IEEE International Conference on Robotics and Automation. Singapore: IEEE, 2017. 2786−2793
[90]	Serban I V, Sankar C, Pieper M, Pineau J, Bengio Y. The bottleneck simulator: a model-based deep reinforcement learning approach. arXiv: 1807.04723, 2018.
[91]	Rashid T, Samvelyan M, de Witt C S, Farquhar G, Foerster J, Whiteson S. QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning. arXiv: 1803.11485, 2018.
[92]	Foerster J N, Chen R Y, Al-Shedivat M, Whiteson S, Abbeel P, Mordatch I. Learning with opponent-learning awareness. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems. Stockholm, Sweden: ACM, 2018. 122−130
[93]	Yuan X, Dong L, Sun C Y. Solver-critic: a reinforcement learning method for discrete-time constrained-input systems. IEEE Transactions on Cybernetics, 2020 doi: 10.1109/TCYB.2020.2978088
[94]	He W, Li Z J, Chen C L P. A survey of human-centered intelligent robots: issues and challenges. IEEE/CAA Journal of Automatica Sinica, 2017, 4(4): 602−609 doi: 10.1109/JAS.2017.7510604
[95]	Nahavandi S. Trusted autonomy between humans and robots: toward human-on-the-loop in robotics and autonomous systems. IEEE Systems, Man, and Cybernetics Magazine, 2017, 3(1): 10−17 doi: 10.1109/MSMC.2016.2623867