mlsanityData to 7 Oct 2026

Foundations of Reinforcement Learning and Control:Connections and New Perspectives

arXiv 2608.02433 ↗First posted 3 Aug 2026cs.LGcited by 0

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Abstract (arXiv)

Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields have common roots in dynamic programming, they have evolved with distinct methodologies, goals, and cultures. Despite decades of mutual influence, a significant gap persists between the two communities. This tutorial introduces adaptive control, actor-critic reinforcement algorithms, and a new way to combine these two paradigms for data-driven decision making on a classical locomotion control problem. Our aim is to provide a foundation for understanding the core differences between the two approaches and insights to help experts in each field better understand and engage with the tools and approaches of the other.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 103 references that are in the map. First posted on arXiv 3 Aug 2026.

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40 of 103 references.

References · 40

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  4. 2018DeepMind Control Suitecited by 935
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  8. 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 482
  9. 2016Control Barrier Function Based Quadratic Programs for Safety Critical Systemscited by 261
  10. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  11. 2017On the Sample Complexity of the Linear Quadratic Regulatorcited by 151
  12. 2025Training Agents Inside of Scalable World Modelscited by 122
28 more
  1. 2019When to use parametric models in reinforcement learning?cited by 118
  2. 2016Why is Posterior Sampling Better than Optimism for Reinforcement Learning?cited by 116
  3. 2022When Is Partially Observable Reinforcement Learning Not Scary?cited by 100
  4. 2017Cautious Model Predictive Control using Gaussian Process Regressioncited by 91
  5. 2022Partially Observable Markov Decision Processes in Robotics: A Surveycited by 81
  6. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 77
  7. 2025MuJoCo Playgroundcited by 57
  8. 2022Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environmentscited by 54
  9. 2023Empirical Design in Reinforcement Learningcited by 54
  10. 2021Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergencescited by 40
  11. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 36
  12. 2020Mitigating Value Hallucination in Dyna Planning via Multistep Predecessor Modelscited by 27
  13. 2021A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learningcited by 26
  14. 2018Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domainscited by 19
  15. 2022GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systemscited by 17
  16. 2023GVFs in the Real World: Making Predictions Online for Water Treatmentcited by 13
  17. 2024Real-Time Recurrent Learning using Trace Units in Reinforcement Learningcited by 10
  18. 2025Partially Observable Reinforcement Learning with Memory Tracescited by 9
  19. 2021A Historical Perspective of Adaptive Control and Learningcited by 8
  20. 2021Off-Policy Actor-Critic with Emphatic Weightingscited by 5
  21. 2025The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspectivecited by 5
  22. 2025Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamicscited by 5
  23. 2025V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functionscited by 5
  24. 2013Information driven self-organization of complex robotic behaviorscited by 4
  25. 2025MPCritic: A plug-and-play MPC architecture for reinforcement learningcited by 3
  26. 2025Embodied Intelligence for Sustainable Flight: A Soaring Robot with Active Morphological Controlcited by 2
  27. 2026Commit to the Bit: Reactive Reinforcement Learning Done Rightcited by 1
  28. 2026Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learningcited by 1

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