Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms
Match-3 puzzle games have garnered significant popularity across all age groups due to their simplicity, non-violent nature, and concise gameplay. However, the development of captivating and well-balanced stages in match-3 puzzle games remains a challenging task for game developers. This study aims...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/21/4456 |
| _version_ | 1797632029809967104 |
|---|---|
| author | Byounggwon Kim Jungyoon Kim |
| author_facet | Byounggwon Kim Jungyoon Kim |
| author_sort | Byounggwon Kim |
| collection | DOAJ |
| description | Match-3 puzzle games have garnered significant popularity across all age groups due to their simplicity, non-violent nature, and concise gameplay. However, the development of captivating and well-balanced stages in match-3 puzzle games remains a challenging task for game developers. This study aims to identify the optimal algorithm for reinforcement learning to streamline the level balancing verification process in match-3 games by comparison with Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms. By training the agent with these two algorithms, the paper investigated which approach yields more efficient and effective difficulty level balancing test results. After the comparative analysis of cumulative rewards and entropy, the findings illustrate that the SAC algorithm is the optimal choice for creating an efficient agent capable of handling difficulty level balancing for stages in a match-3 puzzle game. This is because the superior learning performance and higher stability demonstrated by the SAC algorithm are more important in terms of stage difficulty balancing in match-3 gameplay. This study expects to contribute to the development of improved level balancing techniques in match-3 puzzle games besides enhancing the overall gaming experience for players. |
| first_indexed | 2024-03-11T11:32:08Z |
| format | Article |
| id | doaj.art-e1378a855edd4bf3a24fde153776ce62 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T11:32:08Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e1378a855edd4bf3a24fde153776ce622023-11-10T15:01:30ZengMDPI AGElectronics2079-92922023-10-011221445610.3390/electronics12214456Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic AlgorithmsByounggwon Kim0Jungyoon Kim1Department of Game Media, College of Future Industry, Gachon University, Seongnma-si 13120, Republic of KoreaDepartment of Game Media, College of Future Industry, Gachon University, Seongnma-si 13120, Republic of KoreaMatch-3 puzzle games have garnered significant popularity across all age groups due to their simplicity, non-violent nature, and concise gameplay. However, the development of captivating and well-balanced stages in match-3 puzzle games remains a challenging task for game developers. This study aims to identify the optimal algorithm for reinforcement learning to streamline the level balancing verification process in match-3 games by comparison with Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms. By training the agent with these two algorithms, the paper investigated which approach yields more efficient and effective difficulty level balancing test results. After the comparative analysis of cumulative rewards and entropy, the findings illustrate that the SAC algorithm is the optimal choice for creating an efficient agent capable of handling difficulty level balancing for stages in a match-3 puzzle game. This is because the superior learning performance and higher stability demonstrated by the SAC algorithm are more important in terms of stage difficulty balancing in match-3 gameplay. This study expects to contribute to the development of improved level balancing techniques in match-3 puzzle games besides enhancing the overall gaming experience for players.https://www.mdpi.com/2079-9292/12/21/4456match-3 puzzle gamebalancing testreinforcement learningPPOSAC |
| spellingShingle | Byounggwon Kim Jungyoon Kim Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms Electronics match-3 puzzle game balancing test reinforcement learning PPO SAC |
| title | Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms |
| title_full | Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms |
| title_fullStr | Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms |
| title_full_unstemmed | Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms |
| title_short | Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms |
| title_sort | efficient difficulty level balancing in match 3 puzzle games a comparative study of proximal policy optimization and soft actor critic algorithms |
| topic | match-3 puzzle game balancing test reinforcement learning PPO SAC |
| url | https://www.mdpi.com/2079-9292/12/21/4456 |
| work_keys_str_mv | AT byounggwonkim efficientdifficultylevelbalancinginmatch3puzzlegamesacomparativestudyofproximalpolicyoptimizationandsoftactorcriticalgorithms AT jungyoonkim efficientdifficultylevelbalancinginmatch3puzzlegamesacomparativestudyofproximalpolicyoptimizationandsoftactorcriticalgorithms |