M 2 PA : A Multi-Memory Planning Agent for Open Worlds Inspired by Cognitive Theory (2025)

Abstract: Open-world planning poses a significant challenge for general artificial intelligence due to environmental complexity and task diversity, especially in long-term tasks and lifelong learning. Inspired by cognitive theories, we propose M2PA, an open-world multi-memory planning agent. M2PA innovates by combining Large Language Models (LLMs) with human-like multi-memory systems, aiming to fully leverage the strengths of both while mitigating their respective limitations. By integrating the expansive world knowledge and language processing capabilities of LLMs with the perception and experience accumulation abilities of the human memory system, M2PA exhibits situation awareness, and experience generalization capabilities, as well as the potential for lifelong learning. In experiments, M2PA significantly outperforms current state-of-the-art agents across 50 Minecraft tasks in zero-shot learning. In exploratory lifelong learning experiments, M2PA demonstrates its continuous learning ability, achieving a 38.33% success rate in the “ObtainDiamond” task. Our findings provide a novel paradigm for constructing more effective agents in open-world environments.

## introduction planning @xcite , a core ability of intelligent agents, involves decomposing complex tasks into sub-goals and generating action sequences for each sub-goal. in open-world environments like minecraft (wikipedia contributors, 2024), planning faces new challenges due to complex environments and diverse tasks. recent work @xcite @xcite @xcite shows that large language models (llms) @xcite @xcite with their rich knowledge and reasoning capabilities, offer a new paradigm for open-world planning. however, current approaches directly use raw information as the external knowledge library @xcite or focus only on single-modal memory @xcite , failing to directly connect to agents' experiences or generalize knowledge to unfamiliar situations or novel tasks. human players in games combine the current situation with past experiences for planning and continuously accumulate experiences to improve task success rates. this observation aligns with research on human cognitive systems @xcite @xcite , particularly memory mechanisms @xcite . neuroscience and cognitive psychology reveal that humans possess a complex multi-memory system (figure 1 ) for information storage and recall. when faced with new problems, humans can flexibly invoke and integrate various memory types, such as semantic memory (general knowledge) and episodic memory (specific experiences) @xcite , combining the knowledge required to solve problems with the experiences of the current situation to generate feasible plans. this enables humans to continuously learn, and cope with unfamiliar situations or novel tasks, supporting lifelong learning. however, human semantic memory is inherently limited and leads to constrained planning capabilities. inspired by these, we propose the multi-memory planning agent (m2pa), integrating llms with human-like multi-memory systems. m2pa leverages llms' expansive world knowledge and language processing capabilities while incorporating perception, task-specific experiences, and lifelong learning capabilities from the multi-memory system. this integration enables m2pa to handle immediate contexts while maintaining consistency with long-term goals and past experiences. it emphasizes what human multi-memory systems do best (perception, dynamic knowledge integration, experience utilization, and continuous learning) and what llms do best (massive knowledge representation and efficient retrieval). the m2pa design comprises a semantic memory module based on llms, serving as the primary knowledge source; an episodic memory module using vector databases to abstract successful plans into reusable experiences through innovative encoding, retrieval, and updating mechanisms; a sensory memory with a visual buffer capturing the current scene; a working memory prompter that integrates various memory and feedback, providing richer task prompts for llms. this design enables the agent to utilize semantic, episodic, sensory, and working memory for experience accumulation, situation-aware planning, and lifelong learning. compared to agents based on external knowledge, m2pa offers strong dynamic adaptability (automatic update mechanisms), long-term consistency (decisions and actions are consistent with long-term goals and past experiences), and multimodal information management (seamless integration and utilization of multimodal information). in experiments, m2pa significantly outperforms current state-of-the-art agents across 50 minecraft tasks, which include short-term, medium-term, and long-term tasks. notably, it demonstrates strong planning capabilities in long-term tasks and tackling new challenges without additional training. we also find that the llm, with its richer world knowledge as semantic memory, boosts planning success rates. in addition, lifelong learning experiments show m2pa's potential for continuous learning through step-by-step exploration of the world and experience accumulation, enhancing its planning ability. to summarize, our main contributions are: ## method in this section, we first provide an overview of our proposed m2pa. next, we elaborate on the design of the multi-memory components and then detail how these modules interact within m2pa. ## conclusion this paper presents m2pa, a multi-memory planning agent for open-world environments. m2pa aligns with human multi-memory functions, integrating semantic and episodic memory modules to leverage situation awareness, past experiences, and knowledge to enhance decision-making and planning abilities. experiments demonstrate m2pa's efficiency across short-term, medium-term, and long-term minecraft tasks. it significantly outperforms existing baselines in zero-shot learning. furthermore, m2pa exhibits lifelong learning abilities through dynamic memory updates and optimization. future research will explore m2pa's multi-modal capabilities. it will also refine memory integration and planning abilities, to advance the development of general artificial intelligence.