Agent Ensemble

Agent ensembles represent a powerful approach to improving the performance and adaptability of artificial intelligence systems by combining multiple agents, each potentially employing different models or strategies. Current research focuses on developing efficient ensemble architectures and algorithms, such as those leveraging reinforcement learning to minimize inter-agent disagreement or dynamically selecting agents based on context (e.g., market sentiment or teammate behavior). This approach addresses limitations of single-agent systems in complex tasks, offering enhanced robustness, accuracy, and adaptability across diverse applications, from autonomous vehicle navigation to language-based games and financial trading.