Soccer Simulation 2 Dimensional

Two-dimensional soccer simulation research focuses on developing autonomous agents capable of realistic and effective soccer gameplay within a computationally simulated environment. Current research emphasizes improving agent decision-making through techniques like observation denoising (using predictive modeling and neural networks such as LSTMs and DNNs), enhanced prediction of opponent and teammate actions (e.g., pass prediction), and refined strategies for dribbling, passing, and marking. This work contributes to advancements in multi-agent systems, machine learning, and the development of open-source frameworks that facilitate broader research collaboration and accelerate progress in AI.