This next paper seeks to provide better adjustment of procedural character to adapt to complex terrain.

“…Building on recent progress in deep reinforcement learning (DeepRL), we introduce a mixture of actor-critic experts (MACE) approach that learns terrain-adaptive dynamic locomotion skills using high-dimensional state and terrain descriptions as input, and parameterized leaps or steps as output actions….”

Full paper here.