Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach
PositiveArtificial Intelligence
A new study introduces a sample-efficient reinforcement learning method that enables video game developers to create more realistic AI goalkeepers. This approach addresses the challenges faced by studios with limited resources, allowing them to develop human-like agents without the need for extensive training data. This advancement is significant as it could enhance the gaming experience by making AI opponents more relatable and challenging, ultimately leading to more engaging gameplay.
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