Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
- Title
- Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem
- Creator
- Srinivasaiah R.; Biju V.G.; Jankatti S.K.; Channegowda R.H.; Jinachandra N.S.
- Description
- Blackjack is a classic casino game in which the player attempts to outsmart the dealer by drawing a combination of cards with face values that add up to just under or equal to 21 but are more incredible than the hand of the dealer he manages to come up with. This study considers a simplified variation of blackjack, which has a dealer and plays no active role after the first two draws. A different game regime will be modeled for everyone to ten multiples of the conventional 52-card deck. Irrespective of the number of standard decks utilized, the game is played as a randomized discrete-time process. For determining the optimum course of action in terms of policy, we teach an agent-a decision maker-to optimize across the decision space of the game, considering the procedure as a finite Markov decision chain. To choose the most effective course of action, we mainly research Monte Carlo-based reinforcement learning approaches and compare them with q-learning, dynamic programming, and temporal difference. The performance of the distinct model-free policy iteration techniques is presented in this study, framing the game as a reinforcement learning problem. 2024 Institute of Advanced Engineering and Science. All rights reserved.
- Source
- International Journal of Electrical and Computer Engineering, Vol-14, No. 1, pp. 904-910.
- Date
- 2024-01-01
- Publisher
- Institute of Advanced Engineering and Science
- Subject
- Blackjack; Dynamic programming; Monte Carlo; Q-learning; Reinforcement learning; Temporal difference
- Coverage
- Srinivasaiah R., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST Deemed to be University, Bengaluru, India; Biju V.G., MAI, Faculty of Computer Science and Business Informatics, University of Applied Sciences, Wzburg-Schweinfurt, Germany; Jankatti S.K., Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India; Channegowda R.H., Department of Electronics and Communication Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru, India; Jinachandra N.S., Department of Mechanical Engineering, School of Engineering and Technology, CHRIST Deemed to be University, Bengaluru, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 20888708
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Srinivasaiah R.; Biju V.G.; Jankatti S.K.; Channegowda R.H.; Jinachandra N.S., “Reinforcement learning strategies using Monte-Carlo to solve the blackjack problem,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/13329.