AI based serious game design - Kleptomancy

In this paper, we explored the use of Artificial Intelligence to create an adversary that demonstrates reasonable intelligence through the extensive use of Machine Learning techniques, Deep Reinforcement Learning and Imitation Learning techniques. In particular, we used Proximal Policy Optimizati...

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書目詳細資料
主要作者: Wee, Chang Han
其他作者: Smitha Kavallur Pisharath Gopi
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2024
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在線閱讀:https://hdl.handle.net/10356/175216
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機構: Nanyang Technological University
語言: English
實物特徵
總結:In this paper, we explored the use of Artificial Intelligence to create an adversary that demonstrates reasonable intelligence through the extensive use of Machine Learning techniques, Deep Reinforcement Learning and Imitation Learning techniques. In particular, we used Proximal Policy Optimization (PPO) algorithm, a branch of Model-Free RL Policy Optimization model, as well as Generative Adversarial Imitation Learning (GAIL) to train our intelligent agent. This project aims to evaluate and demonstrate the Intelligent Agent’s adaptive responses and strategies when faced with player-generated challenges in an edutainment game that was developed as part of this project, ‘Kleptomancy’.