Memory-Driven Agent Planning for Long-Horizon Tasks via Hierarchical Encoding and Dynamic Retrieval

This study addresses the challenge of long-term dependency modeling in agent behavior planning for long-horizon tasks and proposes a memory-driven agent planning framework. The method introduces hierarchical memory encoding and dynamic memory retrieval structures, enabling the agent to selectively retain and effectively utilize historical information across multiple time scales, thereby maintaining policy stability and goal consistency in complex dynamic environments. The core idea is to construct an interaction mechanism between short-term and long-term memory, where attention-guided retrieval integrates historical experience with current perception to support continuous planning and decision optimization in long-term tasks. The proposed framework consists of four key modules: perception input, memory encoding, state updating, and behavior generation, forming an end-to-end task-driven learning process. Experimental evaluations based on success rate, average planning steps, memory consistency score, and policy stability demonstrate that the proposed algorithm achieves superior performance in long-term task scenarios, effectively reducing planning redundancy and improving strategy coherence and task efficiency. The results confirm that the memory-driven mechanism provides a novel theoretical foundation and algorithmic framework for developing long-term task agents, establishing a solid basis for adaptive decision-making and continuous planning in complex environments.