AI Soccer

AI soccer research focuses on developing autonomous agents capable of playing realistic and strategic soccer games, either through simulated or real-world robotic platforms. Current research emphasizes multi-agent reinforcement learning (MARL), often employing deep Q-networks or proximal policy optimization, to train agents that learn from raw visual input or simplified state representations. This work contributes to advancements in MARL, computer vision, and robotics, with potential applications in sports analytics, game AI, and the development of more robust and adaptable robotic systems.