Download pdf
Implementing the AlphaZero algorithm for Connect Four: A deep reinforcement learning approach
- 1 Stony Brook Institute at Anhui University, Anhui University, Hefei, 230039, China
* Author to whom correspondence should be addressed.
Abstract
The realm of board games presents a challenging domain for the application of artificial intelligence (AI), given their vast state-action space and inherent complexity. This paper explores the development of a proficient AI for Connect Four using DeepMind's AlphaZero algorithm. The algorithm employs a policy-value network for concurrent prediction of action probabilities and state values, and Monte Carlo Tree Search (MCTS) for decision-making, guided by the policy-value network. Through extensive self-play and data augmentation, our AI learns without the need for explicit prior knowledge. Our experiment demonstrated that the AI player showed significant capability in playing Connect Four, exhibiting strategic decision-making that sometimes-surpassed human performance. These results underline the potential of deep reinforcement learning in advancing AI performance in complex board games.
Keywords
AlphaZero, Connect Four, deep reinforcement learning, monte carlo tree search, policy-value network
[1]. Silver, D., et al. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354-359.
[2]. Silver, D., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144.
[3]. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
[4]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.
[5]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[6]. Mnih, V., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
[7]. Lai, M. (2015). Giraffe: Using deep reinforcement learning to play chess. arXiv preprint arXiv:1509.01549.
[8]. Kocsis, L., Szepesvári, C. (2006) Bandit based Monte-Carlo planning. In: Proceedings of the 17th European conference on Machine Learning. Springer, Berlin, Heidelberg.
[9]. Guo, X., Singh, S., Lee, H., Lewis, R.L., Wang, X. (2014) Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning. In: Advances in neural information processing systems. pp. 3338-3346.
[10]. Browne, C. B., et al. (2012). A survey of Monte Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in games, 4(1), 1-43.
[11]. Guo, X., et al. (2014). Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning. In Advances in neural information processing systems (pp. 3338-3346).
[12]. Silver, D., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144.
[13]. Tesauro, G. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), 58-68.
[14]. Silver, D., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
Cite this article
Guo,Y. (2024). Implementing the AlphaZero algorithm for Connect Four: A deep reinforcement learning approach. Applied and Computational Engineering,33,34-41.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).