Simulators play a key role as testbeds for robotics control algorithms, but deciding when to use them versus collecting real-world data is often treated as an art. We have developed a framework for efficient reinforcement learning (RL) in a scenario where multiple simulators or a target task, each with varying levels of fidelity, are available. Our framework is designed to limit the number of samples used in each successively higher-fidelity/cost simulator by allowing a learning agent to choose to run trajectories at the lowest level simulator that will still provide it with information.
