Correct Me if I am Wrong: Interactive Learning for Robotic Manipulation - Technology Org

Deep reinforcement learning is successfully applied in many real-world robotic tasks. However, it is limited to domains in which a simulator is available or environments that have been tailored and instrumented for the agent's training. Interactive learning approach is useful in training not just industrial robotic systems. Therefore, a recent paper proposes an interactive learning approach in which a human teacher provides evaluative and corrective feedback to the robot during training. The method does not require any reward function and thus avoids credit assignment and reward exploitation issues.