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Multi-agent Reinforcement learning (MARL) studies the behaviour of multiplelearning agents that coexist in a shared environment. MARL is more challengingthan single-agent RL because it involves more complex learning dynamics: theobservations and rewards of each agent are functions of all other agents. Inthe context of MARL, Real-Time Strategy (RTS) games represent very challengingenvironments where multiple players interact simultaneously and control manyunits of different natures all at once. In fact, RTS games are so challengingfor the current RL methods, that just being able to tackle them with RL isinteresting. This project provides the end-to-end experience of applying RL inthe Lux AI v2 Kaggle competition, where competitors design agents to controlvariable-sized fleets of units and tackle a multi-variable optimization,resource gathering, and allocation problem in a 1v1 scenario against othercompetitors. We use a centralized approach for training the RL agents, andreport multiple design decisions along the process. We provide the source codeof the project: https://github.com/roger-creus/centralized-control-lux.

Centralized control for multi-agent RL in a complex Real-Time-Strategy game