Pith Number

pith:N7WH75Z4

pith:2026:N7WH75Z4A5WN6CBCBMJK26TNII

not attested not anchored not stored refs resolved

AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract)

Guanwen Xie, Jingzehua Xu, Shuai Zhang, Xinqi Wang, Yimian Ding, Yiyuan Yang

Incorporating age of information into the state space and adding a wait action lets reinforcement learning produce better policies for underwater vehicles facing observation delays.

arxiv:2605.16777 v1 · 2026-05-16 · eess.SY · cs.SY

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{N7WH75Z4A5WN6CBCBMJK26TNII}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

Simulations show AoI-MDP outperforms the standard MDP, demonstrating superior performance, feasibility, and generalization in underwater tasks.

C2weakest assumption

That adding age of information to the state and a wait action will produce better policies without introducing instability or requiring extensive new hyper-parameter tuning in the reinforcement learning process.

C3one line summary

AoI-MDP integrates age of information into MDP state, action, and reward to optimize decision-making under observation delays for underwater autonomous vehicles.

References

19 extracted · 19 resolved · 1 Pith anchors

[1] Vol and Energy-Aware AUV-Assisted Data Collection for Internet of Underwater Things , year=

[2] Noncooperative Mobile Target Tracking Using Multiple AUVs in Anchor-Free Environments , year=

[3] Environment and Energy-Aware AUV-Assisted Data Collection for the Internet of Underwater Things , year=

[4] Wei, Wei and Wang, Jingjing and Du, Jun and Fang, Zhengru and Ren, Yong and Chen, C. L. Philip , journal=. Differential Game-Based Deep Reinforcement Learning in Underwater Target Hunting Task , year=

[5] Reinforcement Learning and Particle Swarm Optimization Supporting Real-Time Rescue Assignments for Multiple Autonomous Underwater Vehicles , year=

Receipt and verification

First computed	2026-05-20T00:03:21.482424Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6fec7ff73c076cdf08220b12ad7a6d422e223a9f5a6b2644206b99abefde12df

Aliases

arxiv: 2605.16777 · arxiv_version: 2605.16777v1 · doi: 10.48550/arxiv.2605.16777 · pith_short_12: N7WH75Z4A5WN · pith_short_16: N7WH75Z4A5WN6CBC · pith_short_8: N7WH75Z4

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/N7WH75Z4A5WN6CBCBMJK26TNII \
  | 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: 6fec7ff73c076cdf08220b12ad7a6d422e223a9f5a6b2644206b99abefde12df

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6e72fda0fc4a48343e5d961148f43a6d76205158d53e78fc4de8d934f6f4e072",
    "cross_cats_sorted": [
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-05-16T03:16:50Z",
    "title_canon_sha256": "a48508ab65b5ad18b487f6185f18f2e18bc353c00ab2806ab8bc8828baadba51"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16777",
    "kind": "arxiv",
    "version": 1
  }
}