Abstract
“In the realm of classic gaming, Mario has held a special place in the hearts of players for generations. This study, titled ‘Enhancing Mario Gaming using Optimized Reinforcement Learning’, ventures into the uncharted territory of machine learning to elevate the Mario gaming experience to new heights. Our research employs state-of-the-art techniques, including the Proximal Policy Optimization (PPO) algorithm and Convolutional Neural Networks (CNN), to infuse intelligence into the Mario gameplay. By optimizing reinforcement learning, we aim to create an immersive and engaging experience for players. In addition to the technical aspects, we delve into the concept of game appeal, a pivotal component in capturing player engagement. Our innovative approach blends the prowess of PPO, CNN, and reinforcement learning to unlock unique insights and methodologies for enhancing Mario games. This comprehensive analysis provides actionable guidance for selecting the most suitable techniques for distinct facets of Mario games. The culmination is an enriched, captivating, and optimized gaming experience that befits the title, ‘Enhancing Mario Gaming using Optimized Reinforcement Learning’.
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Sah, S.K., Fidele, H. (2024). Enhancing Mario Gaming Using Optimized Reinforcement Learning. In: Devismes, S., Mandal, P.S., Saradhi, V.V., Prasad, B., Molla, A.R., Sharma, G. (eds) Distributed Computing and Intelligent Technology. ICDCIT 2024. Lecture Notes in Computer Science, vol 14501. Springer, Cham. https://doi.org/10.1007/978-3-031-50583-6_14
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