Sample Efficiency
A metric measuring the amount of interaction data a machine learning model requires to reach a target level of performance. Higher sample efficiency means achieving equivalent results with less data. It is a key bottleneck for deploying reinforcement learning in real-world scenarios.
Timeline (last 90 days)
A single farm has only about 18 months of 5-minute-resolution data, and probabilistic model calibration requires more data than deterministic model fitting, so a physics simulator is relied upon to generate synthetic data and fine-tuning is done with a domain adaptation loss.
Sample efficiency and inference efficiency are two independent metrics; CoPE saves interactions during training and maintains single-step actions during inference, with no additional overhead in either.
All Facts (2)
A single farm has only about 18 months of 5-minute-resolution data, and probabilistic model calibration requires more data than deterministic model fitting, so a physics simulator is relied upon to generate synthetic data and fine-tuning is done with a domain adaptation loss.
50%UnverifiedSample efficiency and inference efficiency are two independent metrics; CoPE saves interactions during training and maintains single-step actions during inference, with no additional overhead in either.
50%