Agent Representation

Agent representation focuses on creating effective computational models of agents within various systems, aiming to capture their behavior, interactions, and internal states for improved decision-making and understanding. Current research emphasizes learning disentangled representations that separate agent-specific information from environmental context, often using contrastive learning, graph attention mechanisms, or auxiliary losses integrated with reinforcement learning. These advancements are crucial for improving the efficiency and robustness of artificial intelligence in diverse applications, including robotics, multi-agent systems, and human-computer interaction. The development of accurate and efficient agent representations is driving progress in areas such as offline reinforcement learning and multimodal understanding.