mlsanityData to 7 Oct 2026

Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control

arXiv 2112.06018 ↗First posted 11 Dec 2021cs.LG cs.SY eess.SYcited by 4

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Abstract (arXiv)

We present an architecture where a feedback controller derived on an approximate model of the environment assists the learning process to enhance its data efficiency. This architecture, which we term as Control-Tutored Q-learning (CTQL), is presented in two alternative flavours. The former is based on defining the reward function so that a Boolean condition can be used to determine when the control tutor policy is adopted, while the latter, termed as probabilistic CTQL (pCTQL), is instead based on executing calls to the tutor with a certain probability during learning. Both approaches are validated, and thoroughly benchmarked against Q-Learning, by considering the stabilization of an inverted pendulum as defined in OpenAI Gym as a representative problem.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 12 of its 27 references that are in the map. First posted on arXiv 11 Dec 2021.

Builds on

12 of 27 references.

References · 12

  1. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 361
  2. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 347
  3. 2018Model-Ensemble Trust-Region Policy Optimizationcited by 214
  4. 2019Benchmarking Model-Based Reinforcement Learningcited by 158
  5. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 129
  6. 2019Safe Reinforcement Learning Using Robust MPCcited by 72
  7. 2018Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Map-less Navigation by Leveraging Prior Demonstrationscited by 35
  8. 2019From self-tuning regulators to reinforcement learning and back againcited by 17
  9. 2021Safe Reinforcement Learning Using Robust Action Governorcited by 12
  10. 2020Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approachcited by 10
  11. 2019Driving Reinforcement Learning with Modelscited by 3
  12. 2020Tutoring Reinforcement Learning via Feedback Controlcited by 2

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.

  1. 2023Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learningcited by 5
  2. 2022CT-DQN: Control-Tutored Deep Reinforcement Learningcited by 3
  3. 2025DR-PETS: Learning-Based Control With Planning in Adversarial Environmentscited by 1
  4. 2023In vivo learning-based control of microbial populations density in bioreactorscited by 0