Google Research Football
Physics-based 11-vs-11 football RL environment for training and benchmarking game-playing agents, from the Google Brain team.
Google Research Football
Google Research Football (GFootball) is a reinforcement-learning environment where agents learn to play a full 11-versus-11 game of football (soccer) in an advanced, physics-based 3D simulator. Released by the Google Brain team, it provides a challenging benchmark for research on sample-efficient RL, multi-agent coordination, and long-horizon planning.
Key features
- Physics-based 3D football simulator with configurable difficulty and stochasticity
- Football Academy: a set of progressively harder scenarios (run-to-score, corner, 3-vs-1) for curriculum learning
- OpenAI Gym-compatible API with multiple observation representations (pixels, super-mini-map, floats)
- Built-in reward shaping (scoring plus checkpoints) and single- or multi-agent control
- Reproducible baselines (IMPALA, PPO) with support for self-play and league training
Because a match demands cooperation, opponent modeling, and emergent strategy, GFootball is a rich testbed for NPC behavior and for playtesting AI opponents in an adversarial game setting.
Curated mirror of the open-source Google Research Football (Apache-2.0). Get it from the source.