SGFT uses a simulation-trained value function to guide real-world exploration via potential-based reward shaping and short-horizon objectives, substantially improving fine-tuning sample efficiency.
Further suppose ∆r = max s r(s) − mins r(s) and ∆V = max s Vs(s) − mins Vs(s) are finite
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Rapidly Adapting Policies to the Real World via Simulation-Guided Fine-Tuning
SGFT uses a simulation-trained value function to guide real-world exploration via potential-based reward shaping and short-horizon objectives, substantially improving fine-tuning sample efficiency.