  Domain randomization (DR), which entails training a policy with randomized
dynamics, has proven to be a simple yet effective algorithm for reducing the
gap between simulation and the real world. However, DR often requires careful
tuning of randomization parameters. Methods like Bayesian Domain Randomization
(Bayesian DR) and Active Domain Randomization (Adaptive DR) address this issue
by automating parameter range selection using real-world experience. While
effective, these algorithms often require long computation time, as a new
policy is trained from scratch every iteration. In this work, we propose
Adaptive Bayesian Domain Randomization via Strategic Fine-tuning (BayRnTune),
which inherits the spirit of BayRn but aims to significantly accelerate the
learning processes by fine-tuning from previously learned policy. This idea
leads to a critical question: which previous policy should we use as a prior
during fine-tuning? We investigated four different fine-tuning strategies and
compared them against baseline algorithms in five simulated environments,
ranging from simple benchmark tasks to more complex legged robot environments.
Our analysis demonstrates that our method yields better rewards in the same
amount of timesteps compared to vanilla domain randomization or Bayesian DR.
