Does giving two policies the same seed in a digital twin actually pair them?
research/crn-twin
Seed-paired (common random numbers) policy comparison in a twin: correlation is exactly lost to random-stream desynchronisation (flat score 0.95→0.77, terminal score 0.36→0.001 at q=0.05→0.3), a sizing trap (power 0.11–0.85 vs nominal 0.90), and counter-keyed draws restore ρ=1.
What it found
Common random numbers pair two policies only while their read pointers into the random stream stay aligned. If one policy consumes an extra draw with probability q per step, the paired correlation is exactly ρ = ∑t∑d Bin(t,q)(d) wtwt+d/∑wt² (matched to simulation within 0.012). A flat 20-step score keeps ρ=0.95/0.77 at q=0.05/0.3, but a terminal-only score has ρ=(1−q)T (0.36/0.001). Sizing a comparison as if seeds stayed synchronised (n=12) gives power 0.85 to 0.11 against a nominal 0.90; keying each draw by (episode, step) restores ρ=1. Stylised: Gaussian per-step noise, policies differing only in draw consumption, no robot data.
A figure from its own output
Paper, code and results
More in Digital twins & sim-to-real
- What overshoot does a controller tuned in a fixed-step digital twin really have?What overshoot a controller tuned in a fixed-step twin really has: exact per-step decay laws for explicit/semi-implicit/implicit Euler, damping bias flips sign at
ζ=1/√2(explicit, implicit), a 10% twin target delivers 0.105–0.192 real overshoot (implicit/semi-implicit) or 0.036–0.091 (explicit), error first order inh, Richardson repair. - What does a too-narrow scenario generator do to a digital twin's estimate of a rare failure?A too-narrow scenario generator in a twin makes rare-failure estimates silent: reweighted variance finite iff
s²>½, 0.6× width claims 2e-7 for a real 1.2e-3 and reports zero failures in all runs, optimal width ≈1.05·aneeds 38× fewer runs than plain Monte Carlo, and a defensive real-prior mixture is finite but no better. - What does a state estimator tuned in a twin with the wrong noise levels cost in the real world?An estimator tuned in a twin with wrong noise levels: exact real MSE of the twin's Kalman gain, optimistic claim when the twin is too quiet (+121% at 10×) vs pessimistic when too noisy, no instability cliff, and a Mehra innovation retune with regret ∝ 1/n.
- What does a controller designed in a twin with the wrong actuator gain cost in the real world?A controller designed in a twin with the wrong actuator gain: exact real LQR cost, an instability band the twin does not warn about, closed-loop non-identifiability without dither, and a dithered retune with matched regret law.
- How long does a simulator without actuator limits say an unstable plant stays in control?A limit-free twin of an unstable plant with a saturating actuator overstates mean time to loss of control by 6,295× at noise sd 1 (1.5×10⁶ vs 245 steps), an exponential-in-1/s² gap (slope ≈3.7 real vs ≈12.7–13.2 twin), though both rank the deadbeat gain best.
- What does a Kalman filter tuned in an ideal-sensor twin cost on a quantizing real sensor?A Kalman filter tuned in an ideal-sensor twin, run on a quantizing sensor: twin gain costs +11% (
R=1,D=4) to +64% (R=0.01), SheppardR+D²/12retune is within 1% of best, but its MSE claim under-predicts by up to 43% once the step exceeds the state's scale.
All 164 Digital twins & sim-to-real projects →
Other research topics
Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.