Researchers typically test deep reinforcement learning algorithms on games from Space Invaders to StarCraft. The Google Brain team in Zurich adds another option: football, also known as soccer.

What’s new: Google Research Football allows experiments on a variety of RL techniques in a single environment: self playing, stochastic environment, multi-agent cooperation, and several styles of state representation. Check out the video here.

Key insight: Popular games generally are either easy to win or offer rewards that are too sparse. Most don’t allow for cooperative agents or graduated degrees of difficulty that would help the agents learn basic strategies. Google Research Football is designed to solve all these problems in one go, and it’s open source to boot.

How it works: Karol Kurach and his team provide a physics-based soccer simulator with full-length, 11-player games at a range of difficulty levels. They also offer short scenarios from simple (single player scoring in an empty net) to complex (team coordination to score from a corner kick). Users can build their own scenarios as well.

  • The game state can be represented in three ways: a vector encapsulating 115 features, a full pixel-wise frame, and a “super mini map” of coordinates and speed of every player as well as the ball.
  • Players can perform 16 actions, including directional movement, passing, dribbling, and shooting.
  • The authors implement three state-of-the-art RL algorithms, two using policy gradients (PPO and IMPALA) and one that uses Q-learning (Ape-X DQN), and report their performance on Google Research Football.

Observations: The algorithms supplied quickly solve the easy situations, but they struggle on medium and hard settings even after long periods of training. Performance also depends on the input representation and the number of agents involved.

Why it matters: GRF is a challenge even for today’s best RL algorithms. It gives researchers a multi-agent environment where they can work on improving agents by having them compete with one another, and it provides resources for building more capable agents through increasing degrees of difficulty in an environment that resembles the real world.

We’re thinking: This might be a good time to take to the virtual field and compete for the football leaderboard, as reinforcement learning begins to take on the world’s most popular sport.


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