Summary of Training Interactive Agent in Large Fps Game Map with Rule-enhanced Reinforcement Learning, by Chen Zhang et al.
Training Interactive Agent in Large FPS Game Map with Rule-enhanced Reinforcement Learning
by Chen Zhang, Huan Hu, Yuan Zhou, Qiyang Cao, Ruochen Liu, Wenya Wei, Elvis S. Liu
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Private Military Company Agent (PMCA) system is a novel gaming AI solution for deploying game AI in online multiplayer competitive 3D first-person shooter games, specifically Arena Breakout developed by Tencent Games. The PMCA system is designed to be interactable within a large game map and engages in combat with players while utilizing tactical advantages provided by the surrounding terrain. This approach aims to overcome the challenges of deploying game AI in practical scenarios, particularly in large-scale and complex FPS games. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The PMCA system is designed to enhance gameplay by allowing for more realistic and dynamic interactions between players and AI opponents. The system utilizes terrain-based tactics to gain an advantage in combat, making it a more challenging opponent for human players. This research has the potential to improve the overall gaming experience and provide new opportunities for game developers. |
