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[2602.15274] When Remembering and Planning are Worth it: Navigating under Change
Summary
This article explores how various memory types enhance spatial navigation in changing environments, highlighting the efficiency of agents using advanced memory strategies in uncertain conditions.
Why It Matters
Understanding how memory and planning can improve navigation in dynamic environments is crucial for developing more effective AI systems. This research contributes to the fields of artificial intelligence and machine learning by providing insights into agent behavior in non-stationary contexts, which has implications for robotics and autonomous systems.
Key Takeaways
- Agents that utilize advanced memory strategies outperform simpler models in dynamic environments.
- Non-stationary probability learning techniques enhance memory efficiency and navigation.
- Robust planning and memory use are essential for effective exploration and search tasks.
Computer Science > Artificial Intelligence arXiv:2602.15274 (cs) [Submitted on 17 Feb 2026] Title:When Remembering and Planning are Worth it: Navigating under Change Authors:Omid Madani, J. Brian Burns, Reza Eghbali, Thomas L. Dean View a PDF of the paper titled When Remembering and Planning are Worth it: Navigating under Change, by Omid Madani and 3 other authors View PDF HTML (experimental) Abstract:We explore how different types and uses of memory can aid spatial navigation in changing uncertain environments. In the simple foraging task we study, every day, our agent has to find its way from its home, through barriers, to food. Moreover, the world is non-stationary: from day to day, the location of the barriers and food may change, and the agent's sensing such as its location information is uncertain and very limited. Any model construction, such as a map, and use, such as planning, needs to be robust against these challenges, and if any learning is to be useful, it needs to be adequately fast. We look at a range of strategies, from simple to sophisticated, with various uses of memory and learning. We find that an architecture that can incorporate multiple strategies is required to handle (sub)tasks of a different nature, in particular for exploration and search, when food location is not known, and for planning a good path to a remembered (likely) food location. An agent that utilizes non-stationary probability learning techniques to keep updating its (episodic) memori...
