Animals and robots navigate through environments by building and refining maps of the space. These maps enable functions including navigating back to home, planning, search, and foraging. In large environments, exploration of the space is a hard problem: agents can become stuck in local regions. Here, we use insights from neuroscience to propose and apply the concept of Fragmentation-and-Recall (FarMap), with agents solving the mapping problem by building local maps via a surprisal-based clustering of space, which they use to set subgoals for spatial exploration. Agents build and use a local map to predict their observations; high surprisal leads to a ``fragmentation event'' that truncates the local map. At these events, the recent local map is placed into long-term memory (LTM), and a different local map is initialized. If observations at a fracture point match observations in one of the stored local maps, that map is recalled (and thus reused) from LTM. The fragmentation points induce a natural online clustering of the larger space, forming a set of intrinsic potential subgoals that are stored in LTM as a topological graph. Agents choose their next subgoal from the set of near and far potential subgoals from within the current local map or LTM, respectively. Thus, local maps guide exploration locally, while LTM promotes global exploration. We evaluate FarMap on complex procedurally-generated spatial environments to demonstrate that this mapping strategy much more rapidly covers the environment (number of agent steps and wall clock time) and is more efficient in active memory usage, without loss of performance.

翻译：动物和机器人通过构建并精化空间地图来导航环境。这些地图支持返回巢穴、规划、搜索和觅食等功能。在大型环境中，空间探索是一个难题：智能体可能陷入局部区域。本文借鉴神经科学见解，提出并应用“碎片化与回忆”（Fragmentation-and-Recall，FarMap）概念，使智能体通过基于意外度（surprisal）的空间聚类构建局部地图来解决地图构建问题，并利用这些地图设定空间探索的子目标。智能体构建并使用局部地图来预测其观测结果；高意外度会触发“碎片化事件”，终止当前局部地图。在此类事件中，最近的局部地图被存入长期记忆（LTM），并初始化不同的局部地图。若碎片点的观测与某个存储的局部地图中的观测相匹配，则从LTM中回忆（即重用）该地图。碎片点自然地引发对更大空间的在线聚类，形成一组内在的潜在子目标，这些子目标以拓扑图形式存储在LTM中。智能体分别从当前局部地图或LTM中近程和远程潜在子目标集中选取下一个子目标。因此，局部地图引导局部探索，而LTM促进全局探索。我们在复杂程序生成的 spatial 环境中评估 FarMap，结果表明该地图构建策略能更快速地覆盖环境（以智能体步数和实际时间计），且在活跃内存使用上更高效，且不损失性能。