pith:4E7ME22C
On Data-based Nash Equilibria in LQ Nonzero-sum Differential Games
Data from persistently excited multiagent trajectories yields Nash equilibrium strategies for linear-quadratic nonzero-sum differential games equivalently to model-based methods.
arxiv:2601.11320 v2 · 2026-01-16 · eess.SY · cs.SY
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Claims
It is shown that the proposed data-based solutions of these games are equivalent to known model-based procedures.
The collected data must be persistently excited, and in the stochastic case each player must be able to design a suitable state observer from its own noisy output measurements.
Data-based solutions for Nash equilibria in LQ nonzero-sum differential games are derived and shown equivalent to model-based methods using persistently excited data for both deterministic and stochastic cases.
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Receipt and verification
| First computed | 2026-05-17T23:39:16.628972Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e13ec26b42ac6eac537f2954403b4fb0a5d04b78dad9e4eb79a1acf69527df50
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/4E7ME22CVRXKYU37FFKEAO2PWC \
| 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())"
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Canonical record JSON
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