Reinforcement Learning for Quadruped Locomotion

In adversarial games like VR hunting which involves predators and preys, locomotive behaviour of the non-player character (NPC) is crucial. For effective and realistic quadruped locomotion, major technical contributions of this paper are made to inverse kinematics embedded motion control, quadruped locomotion behaviour adaptation and dynamic environment informed reinforcement learning (RL) of the NPC agent. Behaviour of each NPC can be improved from the top-level decision making such as pursuit and escape down to the actual skeletal motion of bones and joints. The new concepts and techniques are illustrated by a specific use case of predator and prey interaction, in which the objective is to create an intelligent locomotive predator to reach its autonomous steering locomotive prey as fast as possible in all the circumstances. Experiments and comparisons are conducted against the Vanilla dynamic target training; and the RL agent of the quadruped displays more realistic limb movements and produces faster locomotion towards the autonomous steering target.