Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review

TL;DR

This structured review comprehensively covers reinforcement learning principles, advanced algorithms, and their applications in robotics, highlighting recent trends, technical developments, and the pathway toward practical deployment in autonomous systems.

Contribution

It introduces a structured taxonomy of RL applications in robotics and synthesizes recent research trends, bridging theoretical advances with practical robotic implementations.

Findings

DRL algorithms like DDPG, TD3, PPO, SAC are effective for high-dimensional control.

RL applications span locomotion, manipulation, and multi-agent coordination.

The maturity of RL in real-world robotics is increasing.

Abstract

Reinforcement learning (RL) has become a foundational approach for enabling intelligent robotic behavior in dynamic and uncertain environments. This work presents an in-depth review of RL principles, advanced deep reinforcement learning (DRL) algorithms, and their integration into robotic and control systems. Beginning with the formalism of Markov Decision Processes (MDPs), the study outlines essential elements of the agent-environment interaction and explores core algorithmic strategies including actor-critic methods, value-based learning, and policy gradients. Emphasis is placed on modern DRL techniques such as DDPG, TD3, PPO, and SAC, which have shown promise in solving high-dimensional, continuous control tasks. A structured taxonomy is introduced to categorize RL applications across domains such as locomotion, manipulation, multi-agent coordination, and human-robot interaction, along with training methodologies and deployment readiness levels. The review synthesizes recent research efforts, highlighting technical trends, design patterns, and the growing maturity of RL in real-world robotics. Overall, this work aims to bridge theoretical advances with practical implementations, providing a consolidated perspective on the evolving role of RL in autonomous robotic systems.