Keeping Humans in the Loop: Teaching via Feedback in Continuous Action Space Environments
Isaac Sheidlower,Allison Moore,Elaine Short,Isaac Sheidlower,Allison Moore,Elaine Short
Interactive Reinforcement Learning (IntRL) allows human teachers to accelerate the learning process of Reinforcement Learning (RL) robots. However, IntRL has largely been limited to tasks with discrete-action spaces in which actions are relatively slow. This limits IntRL's application to more complicated and challenging robotic tasks, the very tasks that modern RL is particularly well-suited for. ...
