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SAMBA: Safe Model-Based & Active Reinforcement Learning

arXiv 2006.09436 ↗First posted 12 Jun 2020cs.LG cs.AI cs.ROcited by 28

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Abstract (arXiv)

In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. Our method builds upon PILCO to enable active exploration using novel(semi-)metrics for out-of-sample Gaussian process evaluation optimised through a multi-objective problem that supports conditional-value-at-risk constraints. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. Our results show orders of magnitude reductions in samples and violations compared to state-of-the-art methods. Lastly, we provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2020.

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Citing papers per year: 2021: 8; 2022: 8; 2023: 5; 2024: 5; 2025: 2.

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  1. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
  2. 2022Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentationcited by 63
  3. 2021Revisiting Design Choices in Offline Model-Based Reinforcement Learningcited by 50
  4. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 44
  5. 2022Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithmcited by 27
  6. 2022Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interactioncited by 15
  7. 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applicationscited by 12
  8. 2022Provable Safe Reinforcement Learning with Binary Feedbackcited by 7
  9. 2021Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Interventioncited by 5
  10. 2022Effects of Safety State Augmentation on Safe Explorationcited by 5
  11. 2023Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learningcited by 5
  12. 2021DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Interventioncited by 4
16 more
  1. 2021Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planningcited by 3
  2. 2021Model-Based Safe Reinforcement Learning with Time-Varying State and Control Constraints: An Application to Intelligent Vehiclescited by 3
  3. 2021Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe Behaviorscited by 2
  4. 2022Demystifying Reinforcement Learning in Time-Varying Systemscited by 2
  5. 2024Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learningcited by 2
  6. 2025Improving Controller Generalization with Dimensionless Markov Decision Processescited by 2
  7. 2021Learning Robust Controllers Via Probabilistic Model-Based Policy Searchcited by 1
  8. 2022Guided Safe Shooting: model based reinforcement learning with safety constraintscited by 1
  9. 2023Recent Trends in Artificial Intelligence Technology: A Scoping Reviewcited by 1
  10. 2023Modeling Risk in Reinforcement Learning: A Literature Mappingcited by 1
  11. 2024Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processescited by 1
  12. 2025Scaling CrossQ with Weight Normalizationcited by 1
  13. 2023Adaptive Aggregation for Safety-Critical Controlcited by 0
  14. 2023Diverse Priors for Deep Reinforcement Learningcited by 0
  15. 2024GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Modelcited by 0
  16. 2024Diminishing Return of Value Expansion Methodscited by 0