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Recently, reinforcement learning (RL) algorithms have demonstrated remarkablesuccess in learning complicated behaviors from minimally processed input.However, most of this success is limited to simulation. While there arepromising successes in applying RL algorithms directly on real systems, theirperformance on more complex systems remains bottle-necked by the relative datainefficiency of RL algorithms. Domain randomization is a promising direction ofresearch that has demonstrated impressive results using RL algorithms tocontrol real robots. At a high level, domain randomization works by training apolicy on a distribution of environmental conditions in simulation. If theenvironments are diverse enough, then the policy trained on this distributionwill plausibly generalize to the real world. A human-specified design choice indomain randomization is the form and parameters of the distribution ofsimulated environments. It is unclear how to the best pick the form andparameters of this distribution and prior work uses hand-tuned distributions.This extended abstract demonstrates that the choice of the distribution plays amajor role in the performance of the trained policies in the real world andthat the parameter of this distribution can be optimized to maximize theperformance of the trained policies in the real world

How to pick the domain randomization parameters for sim-to-real transfer of reinforcement learning policies?