Pith Number

pith:4MCCMGFO

pith:2026:4MCCMGFO5LQX7QQZVB737RVPGY

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Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance

Adam Haroon, Cody Fleming, Erick J. Rodr\'iguez-Seda, Tristan Schuler

A single RL policy learns both control actions and sparse timing decisions while a Lyapunov run-time assurance shield enforces stability via LQR overrides.

arxiv:2605.12561 v1 · 2026-05-11 · cs.LG · cs.RO

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\pithnumber{4MCCMGFO5LQX7QQZVB737RVPGY}

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

On an inverted pendulum, cart-pole, and planar quadrotor, the learned policy achieves 1.91×, 1.45×, and 3.51× higher mean inter-sample interval (MSI) than a Lyapunov-triggered baseline; a fixed LQR controller at the same average rate is unstable on all three plants, showing that adaptive timing, not a lower average rate, makes sparsity safe.

C2weakest assumption

The approach assumes a known equilibrium point where CARE-based LQR backups and Lyapunov certificates are well-defined, allowing the RTA to provide strictly stronger safety guarantees than expectation-based methods.

C3one line summary

Learned policies with runtime Lyapunov shields achieve substantially higher communication intervals than baselines while maintaining stability on inverted pendulum, cart-pole, and quadrotor systems.

References

31 extracted · 31 resolved · 2 Pith anchors

[1] A. Abels, D. Roijers, T. Lenaerts, A. Nowé, and D. Steckelmacher. Dynamic weights in multi-objective deep reinforcement learning. InInternational Conference on Machine Learning, pages 11–20. PMLR, 201 2019

[2] J. Achiam, D. Held, A. Tamar, and P. Abbeel. Constrained policy optimization. InInternational Conference on Machine Learning, pages 22–31. PMLR, 2017 2017

[3] S. Aggarwal, D. Maity, and T. Ba¸ sar. Interq: A dqn framework for optimal intermittent control. IEEE Control Systems Letters, 2025 2025

[4] M. Alshiekh, R. Bloem, R. Ehlers, B. Könighofer, S. Niekum, and U. Topcu. Safe reinforcement learning via shielding. InProceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018 2018

[5] Altman.Constrained Markov Decision Processes 2021

Receipt and verification

First computed	2026-05-18T03:10:01.975671Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e3042618aeeae17fc219a87fbfc6af36284ed772a4b249bbd9f183c6fb2f23aa

Aliases

arxiv: 2605.12561 · arxiv_version: 2605.12561v1 · doi: 10.48550/arxiv.2605.12561 · pith_short_12: 4MCCMGFO5LQX · pith_short_16: 4MCCMGFO5LQX7QQZ · pith_short_8: 4MCCMGFO

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4MCCMGFO5LQX7QQZVB737RVPGY \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e3042618aeeae17fc219a87fbfc6af36284ed772a4b249bbd9f183c6fb2f23aa

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d524fae40de3911617501cc4d78c9be2d8101223b16693832f708c804f284830",
    "cross_cats_sorted": [
      "cs.RO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-11T23:55:15Z",
    "title_canon_sha256": "441fded9b6c9b07b04ea76f6df2be6887a1930b077af32f3af176b487fc2417b"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12561",
    "kind": "arxiv",
    "version": 1
  }
}