Learning Agile Locomotion via Adversarial Training
Yujin Tang,Jie Tan,Tatsuya Harada,Yujin Tang,Jie Tan,Tatsuya Harada
Developing controllers for agile locomotion is a long-standing challenge for legged robots. Reinforcement learning (RL) and Evolution Strategy (ES) hold the promise of automating the design process of such controllers. However, dedicated and careful human effort is required to design training environments to promote agility. In this paper, we present a multi-agent learning system, in which a quadr...
