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.

Fast Reinforcement Learning is a specialized Python framework designed to accelerate the development and execution of reinforcement learning agents. It offers out-of-the-box support for popular algorithms such as PPO, A2C, DDPG and SAC, combined with high-throughput vectorized environment management. Users can easily configure policy networks, customize training loops and leverage GPU acceleration for large-scale experiments. The library’s modular design ensures seamless integration with OpenAI Gym environments, enabling researchers and practitioners to prototype, benchmark and deploy agents across a variety of control, game and simulation tasks.