pith:GPMBMTWM
The Fragility of Learning LQG Controllers
Any algorithm turning offline trajectories into a stabilizing LQG controller faces an information-theoretic excess-cost lower bound set by the cost Hessian and the exploration policy's Fisher information.
arxiv:2604.24442 v2 · 2026-04-27 · eess.SY · cs.SY
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Claims
We prove an ε-local minimax excess-cost lower bound that applies to any algorithm mapping the offline dataset to a stabilizing linear controller. The bound is expressed in terms of the Hessian of the LQG cost with respect to model parameters and the inverse Fisher Information induced by the exploration policy.
The analysis assumes linear dynamics, quadratic cost, and that all trajectories are generated offline by a known linear exploration policy; if the true system is nonlinear or the policy is nonlinear or adaptive, the bound may not apply.
Proves an ε-local minimax excess-cost lower bound for any algorithm learning a stabilizing linear controller from offline data, expressed via the LQG cost Hessian and inverse Fisher information of the exploration policy, with high complexity for fragile systems.
Receipt and verification
| First computed | 2026-05-20T00:05:45.204725Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
33d8164ecc9fd1fab63cf9a226fe08eb15e0573c7759ef85e961178e53605910
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/GPMBMTWMT7I7VNR47GRCN7QI5M \
| 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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