mlsanityData to 7 Oct 2026

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

arXiv 2006.16210 ↗First posted 29 Jun 2020cs.LG stat.MLcited by 32

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Abstract (arXiv)

We present two elegant solutions for modeling continuous-time dynamics, in a novel model-based reinforcement learning (RL) framework for semi-Markov decision processes (SMDPs), using neural ordinary differential equations (ODEs). Our models accurately characterize continuous-time dynamics and enable us to develop high-performing policies using a small amount of data. We also develop a model-based approach for optimizing time schedules to reduce interaction rates with the environment while maintaining the near-optimal performance, which is not possible for model-free methods. We experimentally demonstrate the efficacy of our methods across various continuous-time domains.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2020.

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Citing papers per year: 2021: 9; 2022: 8; 2023: 6; 2024: 3; 2025: 3; 2026: 3.

32 papers.

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  1. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  2. 2022Multivariate Time Series Forecasting with Dynamic Graph Neural ODEscited by 72
  3. 2023SafeDiffuser: Safe Planning with Diffusion Probabilistic Modelscited by 69
  4. 2021Heavy Ball Neural Ordinary Differential Equationscited by 42
  5. 2021Continuous-Time Model-Based Reinforcement Learningcited by 39
  6. 2023Policy Optimization for Continuous Reinforcement Learningcited by 30
  7. 2022Learning continuous models for continuous physicscited by 28
  8. 2022Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processescited by 23
  9. 2021Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methodscited by 21
  10. 2023Efficient Exploration in Continuous-time Model-based Reinforcement Learningcited by 21
  11. 2022Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rulescited by 15
  12. 2021The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulationcited by 11
20 more
  1. 2021How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studiescited by 10
  2. 2023Neural Laplace Control for Continuous-time Delayed Systemscited by 10
  3. 2022TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speedcited by 8
  4. 2021Continuous-Depth Neural Models for Dynamic Graph Predictioncited by 7
  5. 2023ODE-based Recurrent Model-free Reinforcement Learning for POMDPscited by 6
  6. 2021Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatmentcited by 4
  7. 2024Decoding complexity: how machine learning is redefining scientific discoverycited by 3
  8. 2022Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equationscited by 2
  9. 2021Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learningcited by 1
  10. 2021STR-GODEs: Spatial-Temporal-Ridership Graph ODEs for Metro Ridership Predictioncited by 1
  11. 2023Deep Generative Models for Decision-Making and Controlcited by 1
  12. 2026Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learningcited by 1
  13. 2022Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chainscited by 0
  14. 2024Approximate Control for Continuous-Time POMDPscited by 0
  15. 2024Discovering Continuous-Time Memory-Based Symbolic Policies using Genetic Programmingcited by 0
  16. 2025Increasing Information for Model Predictive Control with Semi-Markov Decision Processescited by 0
  17. 2025Meta-reinforcement learning with minimum attentioncited by 0
  18. 2025SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamicscited by 0
  19. 2026Failure Modes of Deep Multi-Agent RL in Asynchronous Pricing: Reproducible Triggers, Trace Diagnostics, and a Partial Fixcited by 0
  20. 2026Flowing Through States: Neural ODE Regularization for Reinforcement Learningcited by 0