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.

该论文提出了一种名为 FarMap 的映射策略，通过惊奇 - 基于聚类的空间切割来建立本地映射和大脑的记忆，从而有效地解决了在大型环境中的空间探索问题。