Heterogeneous Multi-Agent Reinforcement Learning for Grid-Interactive Communities
Allen Wu, Kingsley Nweye, Zoltán Nagy · 2023
Homogeneous Multi-Agent Reinforcement Learning (MARL) is well studied in games, robots, and simulations. What has not been fully explored is the effectiveness of Heterogeneous MARL in the building space. Heterogeneous MARL has been proven to be more effective than Homogeneous MARL in terms of performance in games. Heterogeneous MARL also has the added benefit of being a more realistic simulation because no two buildings can be expected to react in the same way. Here, we implement the MARLlib library with the CityLearn environment to analyze the benefits of Heterogeneous MARL and compare them to homogeneous agents in a small scale proof of concept.