Dynamic Planning

Dynamic planning focuses on creating adaptable strategies for decision-making in environments with changing conditions, aiming to improve efficiency and robustness in complex tasks. Current research explores diverse approaches, including reinforcement learning algorithms like Dyna-style planning enhanced by meta-gradient methods, large language models (LLMs) integrated with symbolic planners for dynamic plan adjustment, and hierarchical active inference models for understanding biological goal-directed behavior. These advancements have implications for various fields, improving the efficiency of robotic control, autonomous navigation, and human-computer interaction, particularly in scenarios requiring real-time adaptation to unpredictable events.