Multi-agent Reinforcement learning (MARL) studies the behaviour of multiple
learning agents that coexist in a shared environment. MARL is more challenging
than single-agent RL because it involves more complex learning dynamics: the
observations and rewards of each agent are functions of all other agents. In
the context of MARL, Real-Time Strategy (RTS) games represent very challenging
environments where multiple players interact simultaneously and control many
units of different natures all at once. In fact, RTS games are so challenging
for the current RL methods, that just being able to tackle them with RL is
interesting. This project provides the end-to-end experience of applying RL in
the Lux AI v2 Kaggle competition, where competitors design agents to control
variable-sized fleets of units and tackle a multi-variable optimization,
resource gathering, and allocation problem in a 1v1 scenario against other
competitors. We use a centralized approach for training the RL agents, and
report multiple design decisions along the process. We provide the source code
of the project: this https URL

本研究通过在 Lux AI v2 Kaggle 比赛中应用 RL，使用一种集中式方法来训练 RL 代理，并报告了沿途的多个设计决策，以控制多种类型的变量大小编队群，从而解决多优化问题。