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

Andries Smit, Herman A. Engelbrecht, Willie Brink, Arnu Pretorius

Autonomous Agents and Multi-Agent Systems 2023 article cited by 13

importance 3/10 partially solved Reinforcement Learning in Robotics robotics simulation stage: research

Simulated RoboCup football teams still rely on handcrafted strategies, and reinforcement learning had not been scaled to the full 22-player game.

Robotic football is described as a long-standing grand challenge in artificial intelligence.

Agents learned football strategies from tabula rasa via self-play, using an improved multi-agent adaptation of PPO on a custom 2D football simulator faster than the RoboCup simulator.

Agents learned competent football strategies in a full 22-player setting using limited compute (one GPU and one CPU) from tabula rasa through self-play.

Demonstrates full-game MARL is possible under limited compute; no match-win benchmarks claimed.

Venue Autonomous Agents and Multi-Agent Systems
Published 2023
Work type article
Authors Andries Smit, Herman A. Engelbrecht, Willie Brink, Arnu Pretorius
Citations 13 (OpenAlex, at import time)
References 35
Model or method PPO (multi-agent adaptation with policy and critic networks)
Organization not stated in source
Primary source https://doi.org/10.1007/s10458-023-09603-y
Open access hybrid
Keywords multi-agent RL, PPO, self-play, RoboCup, robotic football
3 / 10

incremental / niche

Abstract states robotic football is a long-standing grand challenge in AI, but the addressed problem is handcrafted strategies in simulated RoboCup leagues; a research benchmark with no stated real-world scale.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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