Keeping Humans in the Loop: Teaching via Feedback in Continuous Action Space Environments

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. We seek to bridge this gap by presenting Continuous Action-space Interactive Reinforcement learning (CAIR): the first continuous action-space IntRL algorithm that is capable of using teacher feedback to out-perform state-of-the-art RL algorithms in those tasks. CAIR combines policies learned from the environment and the teacher into a single policy that proportionally weights the two policies based on their agreement. This allows a CAIR agent to learn a relatively stable policy despite potentially noisy or coarse teacher feedback. We validate our approach in two simulated robotics tasks with easy-to-design and - understand heuristic oracle teachers. Furthermore, we validate our approach in a human subjects study through Amazon Mechanical Turk and show CAIR out-performs the prior state-of-the-art in Interactive RL.