mlsanityData to 14 Oct 2026

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

arXiv 2104.10159 ↗First posted 20 Apr 2021cs.AI cs.SY eess.SYcited by 42

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Abstract (arXiv)

Model-based reinforcement learning is a compelling framework for data-efficient learning of agents that interact with the world. This family of algorithms has many subcomponents that need to be carefully selected and tuned. As a result the entry-bar for researchers to approach the field and to deploy it in real-world tasks can be daunting. In this paper, we present MBRL-Lib -- a machine learning library for model-based reinforcement learning in continuous state-action spaces based on PyTorch. MBRL-Lib is designed as a platform for both researchers, to easily develop, debug and compare new algorithms, and non-expert user, to lower the entry-bar of deploying state-of-the-art algorithms. MBRL-Lib is open-source at https://github.com/facebookresearch/mbrl-lib.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). Builds on 29 of its 39 references that are in the map. First posted on arXiv 20 Apr 2021.

Builds on

29 of 39 references.

References · 29

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  4. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  5. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  6. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 773
  7. 2017Deep Reinforcement Learning that Matterscited by 754
  8. 2019Model-Based Reinforcement Learning for Ataricited by 502
  9. 2020dm_control: Software and Tasks for Continuous Controlcited by 375
  10. 2018Model-Ensemble Trust-Region Policy Optimizationcited by 219
  11. 2019Deep Dynamics Models for Learning Dexterous Manipulationcited by 206
  12. 2015Learning Continuous Control Policies by Stochastic Value Gradientscited by 162
17 more
  1. 2019Benchmarking Model-Based Reinforcement Learningcited by 162
  2. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 157
  3. 2017Imagination-Augmented Agents for Deep Reinforcement Learningcited by 140
  4. 2018Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Controlcited by 113
  5. 2015Gaussian Processes for Data-Efficient Learning in Robotics and Controlcited by 98
  6. 2020Objective Mismatch in Model-based Reinforcement Learningcited by 96
  7. 2019Exploring Model-based Planning with Policy Networkscited by 90
  8. 2021On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learningcited by 59
  9. 2020On the role of planning in model-based deep reinforcement learningcited by 57
  10. 2019Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Trafficcited by 50
  11. 2019Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learningcited by 44
  12. 2018The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpointcited by 40
  13. 2019The Differentiable Cross-Entropy Methodcited by 27
  14. 2019Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Modelscited by 26
  15. 2017Learning model-based planning from scratchcited by 22
  16. 2021Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowcited by 2
  17. 2019Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 9; 2023: 16; 2024: 6; 2025: 5; 2026: 2.

42 papers.

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  1. 2022Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentationcited by 69
  2. 2021Revisiting Design Choices in Offline Model-Based Reinforcement Learningcited by 50
  3. 2022Value Gradient weighted Model-Based Reinforcement Learningcited by 32
  4. 2024Hybrid Inverse Reinforcement Learningcited by 32
  5. 2022Training Efficient Controllers via Analytic Policy Gradientcited by 22
  6. 2023Seizing Serendipity: Exploiting the Value of Past Success in Off-Policy Actor-Criticcited by 20
  7. 2026stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluationcited by 20
  8. 2024Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaptioncited by 18
  9. 2021An Experimental Design Perspective on Model-Based Reinforcement Learningcited by 16
  10. 2023Model-Based Uncertainty in Value Functionscited by 15
  11. 2022When to Update Your Model: Constrained Model-based Reinforcement Learningcited by 14
  12. 2023The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithmscited by 12
30 more
  1. 2022Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processescited by 10
  2. 2023How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equationscited by 10
  3. 2022Reinforcement Learning in Presence of Discrete Markovian Context Evolutioncited by 9
  4. 2023A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learningcited by 9
  5. 2023Pearl: A Production-ready Reinforcement Learning Agentcited by 9
  6. 2022Exploration via Planning for Information about the Optimal Trajectorycited by 8
  7. 2025Continual Reinforcement Learning by Planning with Online World Modelscited by 8
  8. 2023Model-Based Reparameterization Policy Gradient Methods: Theory and Practical Algorithmscited by 7
  9. 2023Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangementcited by 6
  10. 2023Value-Distributional Model-Based Reinforcement Learningcited by 5
  11. 2022Continuous Monte Carlo Graph Searchcited by 4
  12. 2023Dynamic Update-to-Data Ratio: Minimizing World Model Overfittingcited by 4
  13. 2023Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Controlcited by 4
  14. 2021Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planningcited by 3
  15. 2023Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretationcited by 3
  16. 2023Goal-conditioned Offline Planning from Curious Explorationcited by 3
  17. 2023Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimizationcited by 3
  18. 2024Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Samplingcited by 3
  19. 2024Zero-Shot Offline Imitation Learning via Optimal Transportcited by 3
  20. 2021Model-Advantage and Value-Aware Models for Model-Based Reinforcement Learning: Bridging the Gap in Theory and Practicecited by 1
  21. 2022Model Based Residual Policy Learning with Applications to Antenna Controlcited by 1
  22. 2023Hybrid Systems Neural Control with Region-of-Attraction Plannercited by 1
  23. 2023Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfacescited by 1
  24. 2024Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learningcited by 1
  25. 2024Three Pathways to Neurosymbolic Reinforcement Learning with Interpretable Model and Policy Networkscited by 1
  26. 2025Situationally-Aware Dynamics Learningcited by 1
  27. 2025Learning Causal Structure Distributions for Robust Planningcited by 1
  28. 2025Double Horizon Model-Based Policy Optimizationcited by 1
  29. 2025Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertioncited by 0
  30. 2026A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learningcited by 0