mlsanityData to 7 Oct 2026

Model-based Offline Quantum Reinforcement Learning

arXiv 2404.10017 ↗First posted 14 Apr 2024quant-ph cs.AI cs.LGcited by 4

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Abstract (arXiv)

This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuits. The model is trained by gradient descent to fit a pre-recorded data set. The policy is optimized with a gradient-free optimization scheme using the return estimate given by the model as the fitness function. This model-based approach allows, in principle, full realization on a quantum computer during the optimization phase and gives hope that a quantum advantage can be achieved as soon as sufficiently powerful quantum computers are available.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 39 references that are in the map. First posted on arXiv 14 Apr 2024.

Builds on

20 of 39 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  4. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  5. 2019Behavior Regularized Offline Reinforcement Learningcited by 654
  6. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  7. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  8. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 328
  9. 2021Generalization in quantum machine learning from few training datacited by 214
  10. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  11. 2018Learning to Walk via Deep Reinforcement Learningcited by 153
  12. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
8 more
  1. 2022A Survey on Quantum Reinforcement Learningcited by 50
  2. 2020Overcoming Model Bias for Robust Offline Deep Reinforcement Learningcited by 42
  3. 2022Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learningcited by 21
  4. 2022Uncovering Instabilities in Variational-Quantum Deep Q-Networkscited by 14
  5. 2022Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learningcited by 9
  6. 2020Interpretable Control by Reinforcement Learningcited by 5
  7. 2021Behavior Constraining in Weight Space for Offline Reinforcement Learningcited by 4
  8. 2023BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploadingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025Benchmarking Quantum Reinforcement Learningcited by 9
  2. 2024Quantum Artificial Intelligence: A Brief Surveycited by 3
  3. 2025From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardwarecited by 0
  4. 2026Staged Hybridisation for Visual Quantum Reinforcement Learning via Knowledge Distillationcited by 0