TopoExplore Enhances AI Agent Exploration with Topology-Aware Navigation.

This research introduces TopoExplore, a novel method designed to enhance the efficiency of AI agent exploration, particularly in environments with complex, enclosed structures. Unlike traditional archive-based or frontier exploration techniques that might waste effort on unreachable areas, TopoExplore integrates a topological analysis. It periodically identifies "voids" or enclosed unexplored regions within the agent's visited map and strategically directs exploration towards their strict entrances. By focusing on these accessible entry points, TopoExplore prevents agents from attempting to enter sealed-off areas. The system applies a decaying bonus to these "gap" or "door" cells, ensuring that once a region is entered, it is no longer prioritized. This intelligent selection mechanism significantly speeds up the discovery of new, relevant areas for learning and planning. Experimental results on MiniGrid environments show TopoExplore achieving a substantial speedup over its Go-Explore baseline, especially in scenarios with multi-interaction doors or decoy structures that can mislead other methods. While it showed a negative result on Montezuma's Revenge without wall knowledge, its performance on HM3D scanned buildings demonstrated its effectiveness in discriminating enclosed structures, proving its value where topological understanding is crucial.