SoccerAgent is a specialized AI framework designed for developing and training autonomous soccer agents using state-of-the-art multi-agent reinforcement learning (MARL) techniques. It simulates realistic soccer matches in 2D or 3D environments, offering tools to define reward functions, customize player attributes, and implement tactical strategies. Users can integrate popular RL algorithms (such as PPO, DDPG, and MADDPG) via built-in modules, monitor training progress through dashboards, and visualize agent behaviors in real time. The framework supports scenario-based training for offense, defense, and coordination protocols. With an extensible codebase and detailed documentation, SoccerAgent empowers researchers and developers to analyze team dynamics and refine AI-driven gameplay strategies for academic and commercial projects.
SoccerAgent Core Features
Multi-agent reinforcement learning environment
Customizable 2D/3D soccer simulations
Built-in support for PPO, DDPG, MADDPG
Real-time training dashboard
Behavior visualization and replay tools
Configurable reward and scenario modules
SoccerAgent Pro & Cons
The Cons
No explicit information about user-friendly interfaces or commercial deployment.
Lack of pricing or commercial service information.
No details on real-time usage or scalability.
The Pros
Comprehensive and holistic multi-agent system that addresses complex multimodal soccer understanding tasks.
Integrates a large-scale multimodal soccer knowledge base (SoccerWiki) supporting knowledge-driven reasoning.
Features a large benchmark (SoccerBench) with diverse and standardized tasks for evaluation and development.
Collaborative reasoning approach enhances performance on soccer-related questions.
Open-source with publicly available code and dataset links.
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