A Literature Review for Non-player Character Existence in Educational Game

Yunanto, Andhik Ampuh; Herumurti, Darlis; Rochimah, Siti; Arifiani, Siska

doi:10.1007/978-981-33-4062-6_20

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…Artificial Intelligence (AI) is standard for most games. Any game with nonplayer character (NPC) [ 23 ] needs the support of the AI system. AI makes NPCs come to life, and players have an immersive feeling in the game world.…”

Section: Methodsmentioning

confidence: 99%

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

Wang

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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This study explores the application of deep reinforcement learning (DRL) in the Internet of Things (IoT) sports game design. The fundamentals of DRL are deeply understood by investigating the current state of IoT fitness applications and the most popular sports game design architectures. The research object is the ball return decision problem of the popular game of table tennis robot return. Deep deterministic policy gradients are proposed by applying DRL to the ball return decision of a table tennis robot. It mainly uses the probability distribution function to represent the optimal decision solution in the Markov Model decision process to optimize the ball return accuracy and network running time. The results show that in the central area of the table, the accuracy of returning the ball is higher, reaching 67.2654%. Different tolerance radii have different convergence curves. When r = 5 cm, the curve converges earlier. After 500,000 iterations, the curve converges, and the accuracy rate is close to 100%. When r = 2 cm and the number of iterations is 800,000, the curve begins to converge, and the accuracy rate reaches 96.9587%. When r = 1 cm, it starts to converge after 800,000 iterations, and the accuracy is close to 56.6953%. The proposed table tennis robot returns the ball in line with the requirements of the actual environment. It has practical application and reference value for developing IoT fitness and sports.

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Section: Methodsmentioning

confidence: 99%