Researchers have introduced a new temporal-difference (TD) framework designed to calibrate black-box simulators. This framework utilizes one-step transitions from a target system and noisy simulator-generated continuation values. The calibration parameter is defined via Bellman orthogonality, which can recover the physical parameter under correct simulator specification or provide a near-optimal L2 approximation of the policy value when misspecified. The paper proposes three TD algorithms based on random-direction finite differences, detailing their convergence properties and error bounds across various query-noise regimes. AI
IMPACT Introduces a novel framework for calibrating simulators, potentially improving the accuracy and reliability of AI systems that rely on simulation.
RANK_REASON The item is an academic paper detailing a new theoretical framework and algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- Bellman
- Kiefer-Wolfowitz
- L2
- Markov
- Polyak--Ruppert
- Student's t-test
- Temporal difference learning
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