Interactive Reinforcement Learning with Inaccurate Feedback

Abstract

Interactive Reinforcement Learning (RL) enables agents to learn from two sources: rewards taken from observations of the environment, and feedback or advice from a secondary critic source, such as human teachers or sensor feedback. The addition of information from a critic during the learning process allows the agents to learn more quickly than non-interactive RL. There are many methods that allow policy feedback or advice to be combined with RL. However, critics can often give imperfect information. In this work, we introduce a framework for characterizing Interactive RL methods with imperfect teachers and propose an algorithm, Revision Estimation from Partially Incorrect Resources (REPaIR), which can estimate corrections to imperfect feedback over time. We run experiments both in simulations and demonstrate performance on a physical robot, and find that when baseline algorithms do not have prior information on the exact quality of a feedback source, using REPaIR matches or improves the expected performance of these algorithms.