Learning to Play Soccer From Scratch: Sample-Efficient Emergent Coordination through Curriculum-Learning and Competition
This addresses the problem of sample-efficient emergent coordination in multi-agent systems for researchers in reinforcement learning and robotics, though it is incremental as it builds on existing methods like TD3 with extensions.
The paper tackled learning complex multi-agent behaviors for 2v2 soccer using deep reinforcement learning, achieving high-quality play in under 40 million interactions through a curriculum-learning approach with stages like 1v0, 1v1, and 2v2.
This work proposes a scheme that allows learning complex multi-agent behaviors in a sample efficient manner, applied to 2v2 soccer. The problem is formulated as a Markov game, and solved using deep reinforcement learning. We propose a basic multi-agent extension of TD3 for learning the policy of each player, in a decentralized manner. To ease learning, the task of 2v2 soccer is divided in three stages: 1v0, 1v1 and 2v2. The process of learning in multi-agent stages (1v1 and 2v2) uses agents trained on a previous stage as fixed opponents. In addition, we propose using experience sharing, a method that shares experience from a fixed opponent, trained in a previous stage, for training the agent currently learning, and a form of frame-skipping, to raise performance significantly. Our results show that high quality soccer play can be obtained with our approach in just under 40M interactions. A summarized video of the resulting game play can be found in https://youtu.be/f25l1j1U9RM.