Single Agent

Single-agent systems, while foundational, are increasingly being augmented or replaced by multi-agent systems to tackle complex problems. Current research focuses on improving the efficiency and scalability of multi-agent reinforcement learning (MARL), often employing techniques like centralized training with decentralized execution, graph neural networks for communication and coordination, and the integration of large language models (LLMs) for enhanced decision-making and knowledge sharing. This shift towards multi-agent approaches is driven by the need to address challenges like partial observability, sparse rewards, and the inherent complexity of real-world scenarios, leading to more robust and efficient solutions across diverse applications.