Scaling multi-agent reinforcement learning to full 11 versus 11 simulated robotic football

Robotic football has long been seen as a grand challenge in artificial intelligence. Despite recent success of learned policies over heuristics and handcrafted rules in general, current teams in the simulated RoboCup football leagues, where autonomous agents compete against each other, still rely on handcrafted strategies with only a few using reinforcement learning directly. This limits a learning agent’s ability to find stronger high-level strategies for the full game. In this paper, we show that it is possible for agents to learn competent football strategies on a full 22 player setting using limited computation resources (one GPU and one CPU), from tabula rasa through self-play. To do this, we build a 2D football simulator with faster simulation times than the RoboCup simulator. We propose various improvements to the standard single-agent PPO training algorithm which help it scale to our multi-agent setting. These improvements include (1) using a policy and critic network with an attention mechanism that scales linearly in the number of agents, (2) sharing networks between agents which allow for faster throughput using batching, and (3) using Polyak averaged opponents, league opponents and freezing the opponent team when necessary. We show through experimental results that stable training in the full 22 player setting is possible. Agents trained in the 22 player setting learn to defeat a variety of handcrafted strategies, and also achieve a higher win rate compared to agents trained in the 4 player setting and evaluated in the full game.