Roadmap of AlphaGo to AlphaStar: Problems and challenges
Abstract
In recent years, deep reinforcement learning is one of the hot topics of artificial intelligence, which can be applied in many fields. However, it also faces many problems and challenges, such as insufficient sample data, large sample space, complex action space and so on. The emergence of AlphaGo solved the problem of large sample space very well. After that, artificial intelligence such as AlphaGo Zero and AlphaStar were released. These intelligent frameworks can be applied in various scenarios. Differentiating from other works, in this paper, we focus on the in-depth analysis of the internal connections of Alpha series from the perspective of the problems and challenges solved to give an insight for the future development of reinforcement learning.
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Combination of different data types processed concurrently enables multimodal models to provide a holistic view, making them especially effective in applications requiring deep and multifaceted understanding of complex scenarios.
“The combination of different data types, processed concurrently, enables these models to provide a holistic view, making them especially effective in applications that require a deep and multifaceted understanding of complex scenarios [113], [161], [162], [160].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [162]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452 · cited 1× · see citing works
“Reinforcement learning, as another research direction of machine learning, has achieved success in the fields of robot control [10], simulation [11], and games [12].”
BackgroundWhere does AlphaGo go: from church-turing thesis to AlphaGo thesis and beyond
Wang, F., Zhang, J. J., Zheng, X., et al. (2016) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2016.7471613 · Cited by 348 · see citing works
Robust Markov Decision Processes
Wiesemann, W., Kuhn, D., & Rustem, B. (2013) · Mathematics of Operations Research · doi:10.1287/moor.1120.0566 · Cited by 312 · see citing works
Arulkumaran, K., Cully, A., & Togelius, J. (2019) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3319619.3321894 · Cited by 155 · see citing works
Overview on DeepMind and Its AlphaGo Zero AI
Holcomb, S. D., Porter, W. K., Ault, S. V., et al. (2018) · International Conference on Big Data and Education (ICBDE) · doi:10.1145/3206157.3206174 · Cited by 113 · see citing works
深度强化学习进展: 从AlphaGo到AlphaGo Zero
赵冬斌, 唐振韬, 邵坤, et al. (2018) · 控制理论与应用 · doi:10.7641/cta.2017.70808 · Cited by 6 · see citing works
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“AlphaGo’s mastery in the game of Go highlighted the effectiveness of deep learning and tree search algorithms within well-defined rule-based environments, underscoring the potential of AI in complex strategy and decision-making [137], [138].”
Background
- Authors
- Rong, Y.
- Affiliations
- Chongqing University of Posts and Telecommunications (CN)
- Corresponding author
- Rong, Yuhan
- Venue
- 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)
- Publisher
- SPIE
- Published
- 2022-11-11
- Pages
- 125
- OpenAlex
- W4308702604
- Cited by
- 2
- References
- 11
- Citations by year
- 2025: 2
- FWCI
- 0.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- AlphaGo, AlphaGo Zero, deep reinforcement learning, game AI
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