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Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila — arXiv:2610.34638

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.34638
License (as archived): CC0-1.0
Archived: 2026-10-27T08:01:34.651Z
SHA-256 of the archived text: 9dec00a51be5f043c568d9deb32190712b3855a3260613adb76bd4927cd01dce
Stable URL of this page: https://metraipolis.com/documento/dc_ee96117273da

How to cite

Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila — arXiv:2610.34638 — arXiv (en), https://arxiv.org/abs/2610.34638 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ee96117273da (Metraipolis archive dc_ee96117273da, sha256 9dec00a51be5f043c568d9deb32190712b3855a3260613adb76bd4927cd01dce)

BibTeX

@misc{dcee96117273da,
  title = {Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila — arXiv:2610.34638},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.34638},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ee96117273da; sha256 9dec00a51be5f043c568d9deb32190712b3855a3260613adb76bd4927cd01dce},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.34638. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Animals have a remarkable capacity for autonomy---driven by intrinsic motivation---without external sensory cues or extrinsic objectives, yet the computational mechanisms behind this ability largely remain a mystery. We combine whole-brain recordings in the fruit fly (Drosophila melanogaster), which reveal neural representations of spontaneous exploratory decisions more than 10 seconds in advance, with a neuromechanical digital twin to test whether optimizing intrinsic goals yields realistically embodied artificial agents that quantitatively match fly behavior and internal representations of future volitional action during exploration. Our hierarchical policy selects from a discrete library of low-level motor primitives, consistent with ethological theories of behavioral syllables. We train agents without external reward using 20 intrinsic motivation algorithms spanning curiosity, entropy, and competency methods. The agents are evaluated in a virtual tethered-ball assay modeled on the fly experiment, comparing spontaneous locomotor statistics and internal representations and decoding future turn direction from kinematics and neural activity on the same timescales observed in flies. DIAYN, a hierarchical competency algorithm learning discrete policies that maximize behavioral entropy and the separability of induced sensory observations, achieves the strongest joint behavioral and neural prospective coding performance relative to flies. Its learned policies closely capture individual variability in fly exploration strategies, suggesting that this diversity serves to maximize state-space coverage at the population level. DIAYN's latent activity also closely captures the sum-difference rule for interhemispheric encoding of kinematics in biological flies. These results establish hierarchical, intrinsically motivated agents as a powerful framework for reverse-engineering animal autonomy.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Neurons and Cognition (q-bio.NC) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.34638 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-27T07:55:30.924Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).