Reinforcement learning in robotic applications: a comprehensive survey

Abstract

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 resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively make self-learning systems. ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain. Therefore, RL must take a key role in the creation of autonomous robotic systems. In recent years, RL has been applied on many platforms of the robotic systems like an air-based, under-water, land-based, etc., and got a lot of success in solving complex tasks. In this paper, a brief overview of the application of reinforcement algorithms in robotic science is presented. This survey offered a comprehensive review based on segments as (1) development of RL (2) types of RL algorithm like; Actor-Critic, DeepRL, multi-agent RL and Human-centered algorithm (3) various applications of RL in robotics based on their usage platforms such as land-based, water-based and air-based, (4) RL algorithms/mechanism used in robotic applications. Finally, an open discussion is provided that potentially raises a range of future research directions in robotics. The objective of this survey is to present a guidance point for future research in a more meaningful direction.