mlsanityData to 7 Oct 2026

Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes

arXiv 1706.06544 ↗First posted 20 Jun 2017stat.ML cs.AI cs.LGcited by 28

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Abstract (arXiv)

We introduce a new formulation of the Hidden Parameter Markov Decision Process (HiP-MDP), a framework for modeling families of related tasks using low-dimensional latent embeddings. Our new framework correctly models the joint uncertainty in the latent parameters and the state space. We also replace the original Gaussian Process-based model with a Bayesian Neural Network, enabling more scalable inference. Thus, we expand the scope of the HiP-MDP to applications with higher dimensions and more complex dynamics.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2017.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 7; 2022: 5; 2023: 4; 2024: 5; 2025: 4; 2026: 2.

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  2. 2022Challenges and Opportunities in Offline Reinforcement Learning from Visual Observationscited by 63
  3. 2022Federated Reinforcement Learning with Environment Heterogeneitycited by 61
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  5. 2021Model-based Meta Reinforcement Learning using Graph Structured Surrogate Modelscited by 16
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  9. 2024Model-based Reinforcement Learning for Parameterized Action Spacescited by 7
  10. 2024Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theorycited by 7
  11. 2024Act as You Learn: Adaptive Decision-Making in Non-Stationary Markov Decision Processescited by 4
  12. 2024DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learningcited by 3
16 more
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  3. 2019Multi-task Batch Reinforcement Learning with Metric Learningcited by 1
  4. 2021Meta-Learning for Koopman Spectral Analysis with Short Time-seriescited by 1
  5. 2021Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles with Joint Radar-Data Communicationscited by 1
  6. 2021Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster Learningcited by 1
  7. 2022Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learningcited by 1
  8. 2023Policy Resilience to Environment Poisoning Attacks on Reinforcement Learningcited by 1
  9. 2025Situationally-Aware Dynamics Learningcited by 1
  10. 2021Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populationscited by 0
  11. 2023On the Benefits of Leveraging Structural Information in Planning Over the Learned Modelcited by 0
  12. 2023Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitationcited by 0
  13. 2025Mapping representations in Reinforcement Learning via Semantic Alignment for Zero-Shot Stitchingcited by 0
  14. 2025Meta-RL with Bayesian Linear Task Modelscited by 0
  15. 2026When Does Test-Time Physical Diagnosis Pay? A Frozen Policy Buys Evidence It Never Readscited by 0
  16. 2026Robust Successor Featurescited by 0