In recent trends, artificial intelligence (AI) is used for the creation of complex automated
control systems. Still, researchers are trying to make a completely autonomous system that …

This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …

Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

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 …

This paper investigates the problem of interactively learning behaviors communicated by a
human teacher using positive and negative feedback. Much previous work on this problem …

A long term goal of Interactive Reinforcement Learning is to incorporate non-expert human
feedback to solve complex tasks. State-of-the-art methods have approached this problem by …

Proximal policy optimization (PPO) is a deep reinforcement learning algorithm based on the
actor–critic (AC) architecture. In the classic AC architecture, the Critic (value) network is used …

Learning from Demonstration (LfD) explores techniques for learning a task policy from
examples provided by a human teacher. The field of LfD has grown into an extensive body …

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 …

Reinforcement learning agent learns how to perform a task by interacting with the
environment. The use of reinforcement learning in real-life applications has been limited …