Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning

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.