mlsanityData to 14 Oct 2026

Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip

arXiv 2609.36347 ↗First posted 28 Sep 2026cs.NE nlin.CD physics.app-phcited by 0

On the map: Deep reinforcement learning methods → Spiking and neuromorphic RL

Abstract (arXiv)

Hardware accelerators based on physical dynamical systems offer an attractive route toward energy-efficient reinforcement learning applications. However, their scalability is challenging because it requires many statistically independent entropy sources. Here, we introduce a quasi-analog decision-making architecture based on asynchronous Boolean networks (or lattices) implemented on a clockless reconfigurable chip. Each node in the network consists of a single logic element that acts as an autonomous entropy source. This architecture gives rise to distributed Boolean chaos, in which a spatially coupled network generates parallel streams of chaotic Boolean transitions with very low statistical dependence between nodes. We experimentally demonstrate parallel decision-making on a 1024-armed bandit problem, which is beyond the scale of previous hardware implementations, while significantly improving power-law scaling performance. Separately, we scale the proposed entropy source to 5120 parallel channels, yielding an aggregate sample generation rate of 2.14 TS/s. Our solution is implemented on a commercial reconfigurable CMOS chip and offers high integration density and ease of programmability. Our results pave the way for using distributed Boolean chaos as a valuable hardware substrate for large-scale reinforcement learning and for the development of fully integrated, high-throughput decision-making accelerators.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Spiking and neuromorphic RL, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 50 references that are in the map. First posted on arXiv 28 Sep 2026.

Builds on

7 of 50 references.

References · 7

  1. 2017Ultrafast photonic reinforcement learning based on laser chaoscited by 13
  2. 2014Efficient Decision-Making by Volume-Conserving Physical Objectcited by 11
  3. 2018Scalable photonic reinforcement learning by time-division multiplexing of laser chaoscited by 8
  4. 2022Controlling chaotic itinerancy in laser dynamics for reinforcement learningcited by 7
  5. 2022Parallel photonic accelerator for decision making using optical spatiotemporal chaoscited by 6
  6. 2015Decision Maker based on Atomic Switchescited by 3
  7. 2023Asymmetric leader-laggard cluster synchronization for collective decision-making with laser networkcited by 2

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