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: https://github.com/roger-creus/centralized-control-lux.

翻译：多智能体强化学习研究多个学习智能体在共享环境中共存的行为。相较于单智能体强化学习，多智能体强化学习更具挑战性，因为其涉及更复杂的学习动态：每个智能体的观测和奖励都是所有其他智能体的函数。在多智能体强化学习背景下，实时策略游戏构成了极具挑战性的环境，其中多个玩家同时交互并同时控制大量不同类型单位。事实上，实时策略游戏对当前强化学习方法来说难度极大，以至于能够用强化学习处理此类游戏本身便具有研究价值。本课题提供了在Lux AI v2 Kaggle竞赛中应用强化学习的端到端经验——在该竞赛中，参赛者需设计智能体以控制可变规模的单位舰队，并在1v1对抗场景下解决多变量优化、资源收集与分配问题。我们采用集中式方法训练强化学习智能体，并报告了过程中的多项设计决策。我们提供了项目源代码：https://github.com/roger-creus/centralized-control-lux。