Betting mechanisms can yield provably more accurate and efficient estimates of real-world robot behavior than Monte Carlo sampling under specified conditions, with practical approximations demonstrated on synthetic data and a robotic manipulator task.
As- sessing transferability from simulation to reality for rein- forcement learning.IEEE transactions on pattern anal- ysis and machine intelligence, 43(4):1172–1183
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Betting for Sim-to-Real Performance Evaluation
Betting mechanisms can yield provably more accurate and efficient estimates of real-world robot behavior than Monte Carlo sampling under specified conditions, with practical approximations demonstrated on synthetic data and a robotic manipulator task.