Heterogeneous Multi Agent Reinforcement Learning

Heterogeneous multi-agent reinforcement learning (HM-ARL) focuses on training multiple agents with diverse capabilities to collaborate effectively in complex environments, overcoming the limitations of traditional methods that assume homogeneous agents. Current research emphasizes developing algorithms that handle varying agent architectures and action spaces, often employing techniques like graph neural networks for communication and specialized parameter sharing strategies (e.g., group-based parameter sharing, latent variable models) to improve efficiency and stability. This field is crucial for advancing applications requiring coordinated action from diverse agents, such as robotics, autonomous driving, and resource management, where homogeneous agent assumptions are unrealistic.