1 |
SUTTON R, BARTO A. Reinforcement learning: an introduction. Cambridge: MIT Press, 1998.
|
2 |
AWHEDA M D, SCHWARTZ H M. The residual gradient FACL algorithm for differential games. Proc. of the Canadian Conference on Electrical and Computer Engineering, 2015: 1006−1011.
|
3 |
JELAI Z. Reinforcement learning based human-prosthetic robot interaction control in movement therapy. Proc. of the International Conference on New Technologies, Development and Application, 2020: 172−181.
|
4 |
LITTMAN M L. Markov games as a framework for multi-agent reinforcement learning. Proc. of the 11th International Conference on Machine Learning, 1994: 157−163.
|
5 |
LI Y, HAN W, WANG Y Q Deep reinforcement learning with application to air confrontation intelligent decision-making of manned/unmanned aerial vehicle cooperative system. IEEE Access, 2020, 8, 67887- 67898.
doi: 10.1109/ACCESS.2020.2985576
|
6 |
DEPTULA P, BELL Z I, DOUCETTE E A, et al Data-based reinforcement learning approximate optimal control for an uncertain nonlinear system with control effectiveness faults. Automatica, 2020, 116, 108922.
doi: 10.1016/j.automatica.2020.108922
|
7 |
GOTTSCHALK S, BURGER M Differences and similarities between reinforcement learning and the classical optimal control framework. Proceedings in Applied Mathematics and Mechanics, 2019, 19 (1): e201900390.
|
8 |
LIAO H C, LIU J S. A model-based reinforcement learning approach to time-optimal control problems. Proc. of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 2019: 657−665.
|
9 |
SHI H B, ZHAI L J, WU H B, et al A multi-tier reinforcement learning model for a cooperative multi-agent system. IEEE Trans. on Cognitive and Developmental Systems, 2020, 12 (3): 636- 644.
doi: 10.1109/TCDS.2020.2970487
|
10 |
NGUYEN N D, NGUYEN T, NAHAVANDI S Multi-agent behavioral control system using deep reinforcement learning. Neurocomputing, 2019, 359 (24): 58- 68.
|
11 |
QIE H, SHI D, SHEN T, et al Joint optimization of multi-UAV target assignment and path planning based on multi-agent reinforcement learning. IEEE Access, 2019, 7, 146264- 146272.
doi: 10.1109/ACCESS.2019.2943253
|
12 |
FIRDAUSIYAH N, TANIGUCHI E, QURESHI A G Modeling city logistics using adaptive dynamic programming based multi-agent simulation. Transportation Research Part E: Logs and Transportation Review, 2019, 125, 74- 96.
doi: 10.1016/j.tre.2019.02.011
|
13 |
REN Y, FAN D M, FENG Q, et al Agent-based restoration approach for reliability with load balancing on smart grids. Applied Energy, 2019, 249, 46- 57.
doi: 10.1016/j.apenergy.2019.04.119
|
14 |
MYERSON R B. Game theory: analysis of conflict. Cambridge: Harvard University Press, 1997.
|
15 |
NIE L, WANG X G, PAN F Y A game-theory approach based on genetic algorithm for flexible job shop scheduling problem. Journal of Physics: Conference Series, 2019, 1187, 032095.
doi: 10.1088/1742-6596/1187/3/032095
|
16 |
WANG X H, ZHONG X X, LI L, et al. PSOGT: PSO and game theoretic based task allocation in mobile edge computing. Proc. of the IEEE 21st International Conference on High Performance Computing and Communications, 2019. DOI: 10.1109/HPCC/SmartCity/DSS. 2019.00318.
|
17 |
XU L, HU B, GUAN Z Z, et al. Multi-agent deep reinforcement learning for pursuit-evasion game scalability. Proc. of the Chinese Intelligent Systems Conference, 2020: 658−669.
|
18 |
ABDOOS M. A cooperative multi-agent system for traffic signal control using game theory and reinforcement learning. IEEE Intelligent Transportation Systems Magazine, 2020. DOI: 10.1109/MITS. 2020.2990189.
|
19 |
BENDOR J, MOOKHERJEE D, RAY D Reinforcement learning in repeated interaction games. Advances in Theoretical Economics, 2001, 3 (2): 159- 174.
doi: 10.2202/1534-5963.1008
|
20 |
CRANDALL J W, GOODRICH M A Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning. Machine Learning, 2011, 82, 281- 314.
doi: 10.1007/s10994-010-5192-9
|
21 |
HU J L, WELLMAN M P. Multiagent reinforcement learning: theoretical framework and an algorithm. Proc. of the 15th International Conference on Machine Learning, 1998: 242−250.
|
22 |
LIU H, LI J F, GE S Y, et al Coordinated scheduling of grid-connected integrated energy microgrid based on multi-agent game and reinforcement learning. Automation of Electric Power Systems, 2019, 43 (1): 58- 66.
|
23 |
XU L, ZHO Z J Channel and power allocation algorithm based on distributed cooperative Q learning. Computer Engineering, 2019, 45 (6): 166- 170, 180.
|
24 |
MATTA M, CARDARILLI G C, NUNZIO L D, et al Q-RTS: a real-time swarm intelligence based on multi-agent Q-learning. Electronics Letters, 2019, 55 (10): 589- 591.
doi: 10.1049/el.2019.0244
|
25 |
CHEN Y, LIU J M, ZHAO H. Social structure emergence: a multi-agent reinforcement learning framework for relationship building. Proc. of the 19th International Conference on Autonomous Agents and Multi-Agent Systems, 2020: 1807−1809.
|
26 |
GE Y Y, ZHU F, HUANG W, et al Multi-agent cooperation Q-learning algorithm based on constrained Markov game. Computer Science and Information Systems, 2020, 17 (2): 647- 664.
doi: 10.2298/CSIS191220009G
|
27 |
DAEICHIAN A, HAGHANI A Fuzzy Q-learning based multi-agent system for intelligent traffic control by a game theory approach. Arabian Journal for Science and Engineering, 2018, 43 (6): 3241- 3247.
doi: 10.1007/s13369-017-3018-9
|
28 |
ULUSOY U, GUZEL M S, BOSTANCI E. A Q-learning-based approach for simple and multi-agent systems. Multi-Agent Systems-Strategies and Applications, 2020. DOI: 10.5772/intechopen. 88484.
|
29 |
HOFBAUER J, SIGMUND K. Evolutionary games and population dynamics. Cambridge: Cambridge University Press, 1998.
|
30 |
NOWAK M A. Evolutionary dynamics: exploring the equations of life. Cambridge: Harvard University Press, 2006.
|
31 |
SMITH J M. Evolution and the theory of games. Cambridge: Cambridge University Press, 1982.
|
32 |
KIMURA M. The neutral theory of molecular evolution. Cambridge: Cambridge University Press, 1983.
|
33 |
CHEN Z H, YANG Z H, WANG H B, et al Overview of reinforcement learning from knowledge expression and handling. Control and Decision, 2008, 23 (9): 962- 975.
|
34 |
GAO Y, CHEN S F, LU X Research on reinforcement learning technology: a review. Acta Automatica Sinica, 2004, 30 (1): 86- 100.
|