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Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations

arXiv 1308.3513 ↗First posted 15 Aug 2013cs.LG cs.AIcited by 68

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Abstract (arXiv)

Control applications often feature tasks with similar, but not identical, dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors, and introduce a semiparametric regression approach for learning its structure from data. In the control setting, we show that a learned HiP-MDP rapidly identifies the dynamics of a new task instance, allowing an agent to flexibly adapt to task variations.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Aug 2013.

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Citing papers per year: 2021: 9; 2022: 19; 2023: 12; 2024: 10; 2025: 11; 2026: 7.

68 papers.

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  1. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 164
  2. 2022Federated Reinforcement Learning with Environment Heterogeneitycited by 61
  3. 2021AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learningcited by 46
  4. 2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 45
  5. 2021Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environmentcited by 41
  6. 2022Contextualize Me -- The Case for Context in Reinforcement Learningcited by 41
  7. 2023Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversationscited by 38
  8. 2021Block Contextual MDPs for Continual Learningcited by 25
  9. 2021Influencing Towards Stable Multi-Agent Interactionscited by 24
  10. 2022A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learningcited by 23
  11. 2023State Regularized Policy Optimization on Data with Dynamics Shiftcited by 23
  12. 2022A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learningcited by 15
38 more
  1. 2022Model-based Lifelong Reinforcement Learning with Bayesian Explorationcited by 12
  2. 2024Nesting Particle Filters for Experimental Design in Dynamical Systemscited by 12
  3. 2025Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Designcited by 12
  4. 2021PantheonRL: A MARL Library for Dynamic Training Interactionscited by 10
  5. 2022Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challengescited by 10
  6. 2022Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learningcited by 10
  7. 2022The Impact of Task Underspecification in Evaluating Deep Reinforcement Learningcited by 10
  8. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
  9. 2022Reinforcement Learning in Presence of Discrete Markovian Context Evolutioncited by 9
  10. 2022All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RLcited by 9
  11. 2023A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learningcited by 9
  12. 2021Meta-Reinforcement Learning by Tracking Task Non-stationaritycited by 8
  13. 2022RILI: Robustly Influencing Latent Intentcited by 8
  14. 2022Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarioscited by 8
  15. 2024Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Datacited by 8
  16. 2022Meta-Learning Parameterized Skillscited by 7
  17. 2024Model-based Reinforcement Learning for Parameterized Action Spacescited by 7
  18. 2022Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variablescited by 6
  19. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 5
  20. 2023iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learningcited by 5
  21. 2023AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systemscited by 5
  22. 2024Offline Reinforcement Learning from Datasets with Structured Non-Stationaritycited by 5
  23. 2024Value of Information and Reward Specification in Active Inference and POMDPscited by 4
  24. 2022Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processescited by 3
  25. 2023Similarity-based Knowledge Transfer for Cross-Domain Reinforcement Learningcited by 3
  26. 2024Offline Reinforcement Learning with Domain-Unlabeled Datacited by 3
  27. 2024Near-Optimal Learning and Planning in Separated Latent MDPscited by 3
  28. 2024Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learningcited by 3
  29. 2025PrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policiescited by 3
  30. 2025Performance Comparisons of Reinforcement Learning Algorithms for Sequential Experimental Designcited by 3
  31. 2022Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learningcited by 2
  32. 2023Off-Policy Evaluation for Action-Dependent Non-Stationary Environmentscited by 2
  33. 2025Improving Controller Generalization with Dimensionless Markov Decision Processescited by 2
  34. 2025Optimal Interactive Learning on the Job via Facility Location Planningcited by 2
  35. 2025Clustering-Based Weight Orthogonalization for Stabilizing Deep Reinforcement Learningcited by 2
  36. 2021Adaptive Adversarial Training for Meta Reinforcement Learningcited by 1
  37. 2022Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learningcited by 1
  38. 2023Policy Resilience to Environment Poisoning Attacks on Reinforcement Learningcited by 1

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