  The performance of reinforcement learning (RL) agents is sensitive to the
choice of hyperparameters. In real-world settings like robotics or industrial
control systems, however, testing different hyperparameter configurations
directly on the environment can be financially prohibitive, dangerous, or time
consuming. We propose a new approach to tune hyperparameters from offline logs
of data, to fully specify the hyperparameters for an RL agent that learns
online in the real world. The approach is conceptually simple: we first learn a
model of the environment from the offline data, which we call a calibration
model, and then simulate learning in the calibration model to identify
promising hyperparameters. We identify several criteria to make this strategy
effective, and develop an approach that satisfies these criteria. We
empirically investigate the method in a variety of settings to identify when it
is effective and when it fails.
