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

arXiv 2510.21758 ↗First posted 11 Oct 2025cs.RO cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 38 of its 94 references that are in the map. First posted on arXiv 11 Oct 2025.

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References · 38

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  2. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  3. 2023A Survey of Reinforcement Learning from Human Feedbackcited by 259
  4. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 228
  5. 2024DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learningcited by 210
  6. 2021How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learnedcited by 201
  7. 2021End-to-End Urban Driving by Imitating a Reinforcement Learning Coachcited by 191
  8. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  9. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  10. 2024Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learningcited by 153
  11. 2021URLB: Unsupervised Reinforcement Learning Benchmarkcited by 138
  12. 2018Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applicationscited by 122
26 more
  1. 2020The Ingredients of Real-World Robotic Reinforcement Learningcited by 111
  2. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  3. 2024DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agentscited by 68
  4. 2020Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learningcited by 61
  5. 2019Data Efficient Reinforcement Learning for Legged Robotscited by 60
  6. 2021Hierarchical Reinforcement Learning By Discovering Intrinsic Optionscited by 57
  7. 2021Deep Reinforcement Learning in Computer Vision: A Comprehensive Surveycited by 42
  8. 2020Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement Learningcited by 36
  9. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Surveycited by 30
  10. 2018Stability-certified reinforcement learning: A control-theoretic perspectivecited by 29
  11. 2020Robotic Grasping using Deep Reinforcement Learningcited by 27
  12. 2019Reinforcement Learning for Batch Bioprocess Optimizationcited by 24
  13. 2022A2C is a special case of PPOcited by 16
  14. 2024Research on Autonomous Robots Navigation based on Reinforcement Learningcited by 12
  15. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learningcited by 12
  16. 2021Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement Learningcited by 10
  17. 2022Reinforcement Learning Algorithms: An Overview and Classificationcited by 10
  18. 2023Pearl: A Production-ready Reinforcement Learning Agentcited by 9
  19. 2020Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approachcited by 8
  20. 2020Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitivescited by 6
  21. 2021Deep Reinforcement Learning for Optimal Stopping with Application in Financial Engineeringcited by 6
  22. 2022Reinforcement Learning in Practice: Opportunities and Challengescited by 5
  23. 2024Deep Reinforcement Learning for Mobile Robot Path Planningcited by 5
  24. 2025Multi-Agent Reinforcement Learning in Intelligent Transportation Systems: A Comprehensive Surveycited by 2
  25. 2025Human vs. Teleoperated Robots in Vineyard Management: A Simulation-Based Analysis of Travel Speed, Routing, and Task Performancecited by 1
  26. 2025VRISE: A Virtual Reality Platfrom for Immersive and Interactive Surveying Educationcited by 1

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