  Physics-based character animation has seen significant advances in recent
years with the adoption of Deep Reinforcement Learning (DRL). However,
DRL-based learning methods are usually computationally expensive and their
performance crucially depends on the choice of hyperparameters. Tuning
hyperparameters for these methods often requires repetitive training of control
policies, which is even more computationally prohibitive. In this work, we
propose a novel Curriculum-based Multi-Fidelity Bayesian Optimization framework
(CMFBO) for efficient hyperparameter optimization of DRL-based character
control systems. Using curriculum-based task difficulty as fidelity criterion,
our method improves searching efficiency by gradually pruning search space
through evaluation on easier motor skill tasks. We evaluate our method on two
physics-based character control tasks: character morphology optimization and
hyperparameter tuning of DeepMimic. Our algorithm significantly outperforms
state-of-the-art hyperparameter optimization methods applicable for
physics-based character animation. In particular, we show that hyperparameters
optimized through our algorithm result in at least 5x efficiency gain comparing
to author-released settings in DeepMimic.
