O. Vinyals, August, 2017, Starcraft II: A New Challenge for Reinforcement Learning, arXiv preprint arXiv:1708.04782

G. Andrade, July, 2005, Extending reinforcement learning to provide dynamic game balancing, Proceedings of the Work- shop on Reasoning, Representation and Learning in Com- puter Games, 19th International Joint Conference on Artificial Intelligence (IJCAI), pp. 7-12

R. S. Sutton, A. G. Barto, 2018, Reinforcement learning: An introduction, MIT press

V. Mnih, December, 2013, Playing atari with deep reinforcement learning, arXiv preprint arXiv:1312.5602

L. Torrey, J. Shavlik, 2010, Transfer learning, Handbook of research on machine learning applications and trends: algori- thms, methods, and techniques, IGI global, pp. 242-264

V. Mnih, June, 2016, Asynchronous methods for deep reinforce- ment learning, Proceedings of The 33rd International conference on machine learning PMLR, Vol. 48, pp. 1928-1937

V. R. Konda, J. N. Tsitsiklis, December, 2000, Actor-critic algorithms, Advances in neural information processing systems

J. Redmon, A. Farhadi, April, 2018, YOLOv3: An Incremental Improvement, arXiv preprint arXiv:1804.02767

J. S. Lee, October, 2020, CNN based Reinforcement Learning for Driving Behavior of Simulated Self-Driving Car, TThe Korean Institute of Electrical Engineers (KIEE), Vol. 69, No. 11, pp. 1740-1749

K. Zhang, Z. Yang, T. Basar, June 2021, Multi-agent reinforce- ment learning: A selective overview of theories and algorithms, Handbook of Reinforcement Learning and Control, pp. 321-384

X. J. Wang, December 2021, SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II, Pro- ceedings of the 38th International Conference on Machine Learning, PMLR, Vol. 139, pp. 10905-10915

T. H. Lee, November 2021, YOLOv3 based Reinforcement learning for mobile game playing policy, Autumn Annual Con- ference of the Institute of Electronics and Information Engineers(IEIE)