Creating human-like autonomous players in real-time first person shooter computer games
This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents,...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2009
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/6169 https://ink.library.smu.edu.sg/context/sis_research/article/7172/viewcontent/261_3920_1_PB.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the Turing test for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like human and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of humanlike, the overall performance of our agent was encouraging as it acquired the highest game score while staying convincing to be human-like in some judges’ opinions. |
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