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All On-Board: Fully On-Chip Neuromorphic Q-Learning with Embedded CartPole Simulation — arXiv:2609.32317

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.32317
License (as archived): CC0-1.0
Archived: 2026-10-07T22:39:38.405Z
SHA-256 of the archived text: 61d82359d9709e195d8ddc870423002e6d6721d9a4bd500efd9f1dbf9e7754cc
Stable URL of this page: https://metraipolis.com/documento/dc_d4dfee376f6d

How to cite

All On-Board: Fully On-Chip Neuromorphic Q-Learning with Embedded CartPole Simulation — arXiv:2609.32317 — arXiv (en), https://arxiv.org/abs/2609.32317 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d4dfee376f6d (Metraipolis archive dc_d4dfee376f6d, sha256 61d82359d9709e195d8ddc870423002e6d6721d9a4bd500efd9f1dbf9e7754cc)

BibTeX

@misc{dcd4dfee376f6d,
  title = {All On-Board: Fully On-Chip Neuromorphic Q-Learning with Embedded CartPole Simulation — arXiv:2609.32317},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.32317},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d4dfee376f6d; sha256 61d82359d9709e195d8ddc870423002e6d6721d9a4bd500efd9f1dbf9e7754cc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.32317. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

As AI models grow in size and usage, their energy demands increase dramatically, raising sustainability and economic concerns. Neuromorphic hardware, inspired by the energy efficiency of the brain, seeks to address this challenge by offering low-power, fast-processing alternatives to conventional computing. Such hardware is particularly well-suited to control systems deployed in resource-constrained environments, which are best trained via reinforcement learning (RL). This contribution presents the design and implementation of a fully on-chip, closed-loop Loihi 2 RL agent. Our neuromorphic circuit consists of a fully embedded Q-learning algorithm and an on-chip simulation of the CartPole-v0 environment on Loihi 2. Our Q-learning algorithm trained the same number of successful agents as the CPU implementation in only half the execution time and with two orders of magnitude less dynamic power. These findings demonstrate the viability of RL on neuromorphic hardware and highlight its promise for building energy-efficient, real-time, embedded AI systems.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Neural and Evolutionary Computing (cs.NE) ; Machine Learning (cs.LG); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.32317 — 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 2026-10-07T22:14:28.597Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).