State Representation

State representation in artificial intelligence focuses on creating effective internal models of an environment's state, enabling agents to make informed decisions. Current research emphasizes learning these representations automatically, often using contrastive learning, deep reinforcement learning, and large language models (LLMs) to extract meaningful features from high-dimensional data, including images and text, and to improve generalization and sample efficiency. These advancements are crucial for improving the robustness and adaptability of AI agents in complex, dynamic environments, with applications ranging from robotics and recommender systems to autonomous driving and natural language processing.