In this thesis, we explore the application of the Multi-Agent Actor-Critic (MAAC) algorithm in the context of energy management systems within buildings, where multiple agents must cooperate and compete to optimize energy usage. Efficient energy management in buildings is a challenging problem, often requiring coordination among multiple systems—such as heating, ventilation, and power generation—that interact in complex ways. Reinforcement learning (RL) offers a promising solution by enabling agents to learn optimal policies through trial and error. However, the non-stationary, mixed cooperative-competitive nature of this environment complicates the learning process, as agents must adapt not only to the environment but also to the evolving strategies of other agents.