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Safe Exploration of State and Action Spaces in Reinforcement Learning

arXiv 1402.0560 ↗First posted 4 Feb 2014cs.LG cs.AIcited by 37

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Abstract (arXiv)

In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks, where the trial and error process may lead to the selection of actions whose execution in some states may result in damage to the learning system (or any other system). Consequently, when an agent begins an interaction with a dangerous and high-dimensional state-action space, an important question arises; namely, that of how to avoid (or at least minimize) damage caused by the exploration of the state-action space. We introduce the PI-SRL algorithm which safely improves suboptimal albeit robust behaviors for continuous state and action control tasks and which efficiently learns from the experience gained from the environment. We evaluate the proposed method in four complex tasks: automatic car parking, pole-balancing, helicopter hovering, and business management.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2014.

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Citing papers per year: 2021: 5; 2022: 9; 2023: 12; 2024: 6; 2025: 3; 2026: 2.

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  1. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
  2. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 158
  3. 2023A Survey on Physics Informed Reinforcement Learning: Review and Open Problemscited by 22
  4. 2021A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processescited by 17
  5. 2021There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learningcited by 17
  6. 2022Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interactioncited by 15
  7. 2023A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviorscited by 13
  8. 2021Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal controlcited by 12
  9. 2024Safe Reinforcement Learning on the Constraint Manifold: Theory and Applicationscited by 12
  10. 2023Learning Human-like Representations to Enable Learning Human Valuescited by 7
  11. 2021Understanding and Avoiding AI Failures: A Practical Guidecited by 6
  12. 2022Safe Deep RL in 3D Environments using Human Feedbackcited by 5
25 more
  1. 2022Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement Learningcited by 5
  2. 2023Model Based Reinforcement Learning for Personalized Heparin Dosingcited by 5
  3. 2024Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learningcited by 4
  4. 2025HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agentscited by 4
  5. 2022Safe Online Bid Optimization with Return on Investment and Budget Constraintscited by 3
  6. 2021Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robotscited by 1
  7. 2022Robust AI Driving Strategy for Autonomous Vehiclescited by 1
  8. 2023Heterogeneous Knowledge for Augmented Modular Reinforcement Learningcited by 1
  9. 2023Efficient Open-world Reinforcement Learning via Knowledge Distillation and Autonomous Rule Discoverycited by 1
  10. 2023Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesiscited by 1
  11. 2024Cyber Physical Gamescited by 1
  12. 2025Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Searchcited by 1
  13. 2022SCOPE: Safe Exploration for Dynamic Computer Systems Optimizationcited by 0
  14. 2022Don't do it: Safer Reinforcement Learning With Rule-based Guidancecited by 0
  15. 2023Imitating careful experts to avoid catastrophic eventscited by 0
  16. 2023Constrained Exploration in Reinforcement Learning with Optimality Preservationcited by 0
  17. 2023Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)cited by 0
  18. 2023How to ensure a safe control strategy? Towards a SRL for urban transit autonomous operationcited by 0
  19. 2023Meta-learning to Calibrate Gaussian Processes with Deep Kernels for Regression Uncertainty Estimationcited by 0
  20. 2024A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object trackingcited by 0
  21. 2024A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environmentscited by 0
  22. 2024Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learningcited by 0
  23. 2025Safely Learning Controlled Stochastic Dynamicscited by 0
  24. 2026Directional Constraints for Efficient Exploration in Safe Reinforcement Learningcited by 0
  25. 2026Specifying Reward Functions for RL Without Environment Samplingcited by 0