  Equipping characters with diverse motor skills is the current bottleneck of
physics-based character animation. We propose a Deep Reinforcement Learning
(DRL) framework that enables physics-based characters to learn and explore
motor skills from reference motions. The key insight is to use loose space-time
constraints, termed spacetime bounds, to limit the search space in an early
termination fashion. As we only rely on the reference to specify loose
spacetime bounds, our learning is more robust with respect to low quality
references. Moreover, spacetime bounds are hard constraints that improve
learning of challenging motion segments, which can be ignored by imitation-only
learning. We compare our method with state-of-the-art tracking-based DRL
methods. We also show how to guide style exploration within the proposed
framework
