Environment Dynamic

Environment dynamics research focuses on understanding and modeling how environments change over time and space, impacting the performance of agents interacting within them. Current research emphasizes developing robust models, including those based on diffusion models, graph-based representations, and model-predictive control, to accurately predict and adapt to these changes, often incorporating techniques like hierarchical reinforcement learning and multi-objective optimization. This work is crucial for improving the efficiency and reliability of artificial intelligence in various applications, such as robotics, autonomous navigation, and reinforcement learning, where accurate environment modeling is essential for successful operation. The development of more accurate and adaptable models is driving progress across multiple fields.