Fast Adaptation of Deep Reinforcement Learning-Based Navigation Skills to Human Preference
Jinyoung Choi,Christopher Dance,Jung-eun Kim,Kyung-sik Park,Jaehun Han,Joonho Seo,Minsu Kim,Jinyoung Choi,Christopher Dance,Jung-eun Kim,Kyung-sik Park,Jaehun Han,Joonho Seo,Minsu Kim
Deep reinforcement learning (RL) is being actively studied for robot navigation due to its promise of superior performance and robustness. However, most existing deep RL navigation agents are trained using fixed parameters, such as maximum velocities and weightings of reward components. Since the optimal choice of parameters depends on the use-case, it can be difficult to deploy such existing meth...
