Interactive Reinforcement Learning with Inaccurate Feedback
Taylor A. Kessler Faulkner,Elaine Schaertl Short,Andrea L. Thomaz,Taylor A. Kessler Faulkner,Elaine Schaertl Short,Andrea L. Thomaz
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...
