Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL

Mahsa Bastankhah, Grace Liu, Dilip Arumugam, Thomas L. Griffiths, Benjamin Eysenbach
10/15/2025
cs.LG

No official code found. Best implementation: Mahsa-Bastankhah/demystifying-single-goal-exploration-code (7 stars, last commit 2025-10-13).

Abstract

In this work, we take a first step toward elucidating the mechanisms behind emergent exploration in unsupervised reinforcement learning. We study Single-Goal Contrastive Reinforcement Learning (SGCRL), a self-supervised algorithm capable of solving challenging long-horizon goal-reaching tasks without external rewards or curricula. We combine theoretical analysis of the algorithm's objective function with controlled experiments to understand what drives its exploration. We show that SGCRL maximizes implicit rewards shaped by its learned representations. These representations automatically modify the reward landscape to promote exploration before reaching the goal and exploitation thereafter. Our experiments also demonstrate that these exploration dynamics arise from learning low-rank representations of the state space rather than from neural network function approximation. Our improved understanding enables us to adapt SGCRL to perform safety-aware exploration.

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Codebase of the paper "Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL"

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Cite this paper

@article{bastankhah2025demystifying,
  title  = {Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL},
  author = {Mahsa Bastankhah and Grace Liu and Dilip Arumugam and Thomas L. Griffiths and Benjamin Eysenbach},
  year   = {2025},
  eprint = {2510.14129},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.14129v1}
}

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