Diversity is Strength: Mastering Football Full Game with Interactive Reinforcement Learning of Multiple AIs

Training AI with strong and rich strategies in multi-agent environments remains an important research topic in Deep Reinforcement Learning (DRL). The AI's strength is closely related to its diversity of strategies, and this relationship can guide us to train AI with both strong and rich strategies. To prove this point, we propose Diversity is Strength (DIS), a novel DRL training framework that can simultaneously train multiple kinds of AIs. These AIs are linked through an interconnected history model pool structure, which enhances their capabilities and strategy diversities. We also design a model evaluation and screening scheme to select the best models to enrich the model pool and obtain the final AI. The proposed training method provides diverse, generalizable, and strong AI strategies without using human data. We tested our method in an AI competition based on Google Research Football (GRF) and won the 5v5 and 11v11 tracks. The method enables a GRF AI to have a high level on both 5v5 and 11v11 tracks for the first time, which are under complex multi-agent environments. The behavior analysis shows that the trained AI has rich strategies, and the ablation experiments proved that the designed modules benefit the training process.

Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning

Asymmetrical multiplayer (AMP) game is a popular game genre which involves multiple types of agents competing or collaborating with each other in the game. It is difficult to train powerful agents that can defeat top human players in AMP games by typical self-play training method because of unbalancing characteristics in their asymmetrical environments. We propose asymmetric-evolution training (AET), a novel multi-agent reinforcement learning framework that can train multiple kinds of agents simultaneously in AMP game. We designed adaptive data adjustment (ADA) and environment randomization (ER) to optimize the AET process. We tested our method in a complex AMP game named Tom \& Jerry, and our AIs trained without using any human data can achieve a win rate of 98.5% against top human players over 65 matches. The ablation experiments indicated that the proposed modules are beneficial to the framework.

Macro action selection with deep reinforcement learning in StarCraft

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also considered as a testbed for AI research, due to its enormous state space, hidden information, multi-agent collaboration and so on. Thanks to the annual AIIDE and CIG competitions, a growing number of bots are proposed and being continuously improved. However, a big gap still remains between the top bot and the professional human players. One vital reason is that current bots mainly rely on predefined rules to perform macro actions. These rules are not scalable and efficient enough to cope with the large but partially observed macro state space in SC. In this paper, we propose a DRL based framework to do macro action selection. Our framework combines the reinforcement learning approach Ape-X DQN with Long-Short-Term-Memory (LSTM) to improve the macro action selection in bot. We evaluate our bot, named as LastOrder, on the AIIDE 2017 StarCraft AI competition bots set. Our bot achieves overall 83% win-rate, outperforming 26 bots in total 28 entrants.