Human-Like Goalkeeping in a Realistic Football Simulation: a Sample-Efficient Reinforcement Learning Approach
arXiv:2510.23216v4 Announce Type: replace-cross Abstract: While several high profile video games have served as testbeds for Deep Reinforcement Learning (DRL), this technique has rarely been employed by the game industry for crafting authentic AI behaviors. Previous research focuses on training super-human agents with large models, which is impractical for game studios with limited resources aiming for human-like agents. This paper proposes a sample-efficient DRL method tailored for training and fine-tuning agents in industrial settings such as the video game industry. Our method improves samp
The increasing maturity of Deep Reinforcement Learning (DRL) and the demand for more sophisticated, human-like AI behaviors in entertainment and simulation drive this research.
This development indicates a practical pathway for integrating advanced, resource-efficient AI into commercial products, potentially democratizing sophisticated AI agent development for industries beyond the tech giants.
The focus shifts from creating superhuman, resource-intensive AI models to developing human-like, sample-efficient agents that are viable for widespread industry adoption, particularly in gaming and simulation.
- · Video game industry
- · AI-powered simulation companies
- · Reinforcement learning researchers
- · AI game developers
- · Companies reliant on simple scripting for in-game AI
- · Developers solely focused on large, computationally expensive AI models
More realistic and engaging AI behaviors become standard in video games and interactive simulations.
The techniques developed here could be adapted for training AI agents in other domains requiring human-like nuance, such as robotics or digital assistants.
This could accelerate the creation of highly believable virtual characters and agents, blurring lines between real and simulated interactions.
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Read at arXiv cs.LG