TopoExplore: Homology as an Exploration Signal
Exploration signals in reinforcement learning are currently computed from what an agent has seen: visitation counts, density estimates, or a model's prediction error at individual states. None of these report the holes in the visitation space. To capture them, we compute the persistent homology of the agent's own archive of visited states during exploration and turn each detected class (an enclosed region the archive surrounds but has not entered) into a selection bonus concentrated on the archived states from which entry is possible, gated by an attempt counter that retires candidate entrances and prunes sealed structures. The method, TopoExplore, is Go-Explore plus one additive term, so...
| Authors | Jason Carlson |
|---|---|
| Primary category | cs.AI |
| Published | 10 July 2026 |
| Last updated at source | 26 September 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.