Reinforcement learning is a branch of machine learning concerned with using experience
gained through interacting with the world and evaluative feedback to improve a system's …

Those designing autonomous systems that interact with humans will invariably face
questions about how humans think and make decisions. Fortunately, computational …

Conventional feedback control methods can solve various types of robot control problems
very efficiently by capturing the structure with explicit models, such as rigid body equations …

Interactive Imitation Learning in Robotics: A Survey Page 1 Interactive Imitation Learning in
Robotics: A Survey Page 2 Other titles in Foundations and Trends® in Robotics A Survey on …

It is often difficult to hand-specify what the correct reward function is for a task, so
researchers have instead aimed to learn reward functions from human behavior or …

While recent advances in deep reinforcement learning have allowed autonomous learning
agents to succeed at a variety of complex tasks, existing algorithms generally require a lot …

Human-centered reinforcement learning (RL), in which an agent learns how to perform a
task from evaluative feedback delivered by a human observer, has become more and more …

Emerging AI systems will be making more and more decisions that impact the lives of
humans in a significant way. It is essential, then, that these AI systems make decisions that …

Machine translation is a natural candidate problem for reinforcement learning from human
feedback: users provide quick, dirty ratings on candidate translations to guide a system to …

Humans often attempt to influence one another's behavior using rewards and punishments.
How does this work? Psychologists have often assumed that “evaluative feedback” …