mlsanityData to 14 Oct 2026

A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration

arXiv 2504.11907 ↗First posted 16 Apr 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → Safe and constrained RL

Abstract (arXiv)

Autonomous exploration of cluttered environments requires efficient exploration strategies that guarantee safety against potential collisions with unknown random obstacles. This paper presents a novel approach combining a graph neural network-based exploration greedy policy with a safety shield to ensure safe navigation goal selection. The network is trained using reinforcement learning and the proximal policy optimization algorithm to maximize exploration efficiency while reducing the safety shield interventions. However, if the policy selects an infeasible action, the safety shield intervenes to choose the best feasible alternative, ensuring system consistency. Moreover, this paper proposes a reward function that includes a potential field based on the agent's proximity to unexplored regions and the expected information gain from reaching them. Overall, the approach investigated in this paper merges the benefits of the adaptability of reinforcement learning-driven exploration policies and the guarantee ensured by explicit safety mechanisms. Extensive evaluations in simulated environments demonstrate that the approach enables efficient and safe exploration in cluttered environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Safe and constrained RL, one of 15 directions in Deep reinforcement learning methods. Builds on 24 of its 40 references that are in the map. First posted on arXiv 16 Apr 2025.

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24 of 40 references.

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Same direction, newest first

  1. 2024Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey22 Apr

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2021How Attentive are Graph Attention Networks?cited by 835
  5. 2021Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learningcited by 371
  6. 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
  7. 2021A Survey of Exploration Methods in Reinforcement Learningcited by 73
  8. 2023State-wise Safe Reinforcement Learning: A Surveycited by 55
  9. 2023SafeDreamer: Safe Reinforcement Learning with World Modelscited by 54
  10. 2022skrl: Modular and Flexible Library for Reinforcement Learningcited by 49
  11. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 36
  12. 2021Goal-Driven Autonomous Exploration Through Deep Reinforcement Learningcited by 35
11 more
  1. 2021Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulationcited by 35
  2. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 30
  3. 2022Evaluating Model-free Reinforcement Learning toward Safety-critical Taskscited by 22
  4. 2022CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learningcited by 20
  5. 2022Reinforcement learning on graphs: A surveycited by 20
  6. 2023ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Explorationcited by 18
  7. 2021Conservative and Adaptive Penalty for Model-Based Safe Reinforcement Learningcited by 16
  8. 2022Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and Deep-reinforcement-learning-based Autonomous Explorationcited by 8
  9. 2023Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learningcited by 5
  10. 2025Multi-Robot System for Cooperative Exploration in Unknown Environments: A Surveycited by 2
  11. 2024Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Searchcited by 1

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  4. 2024A Safe Exploration Strategy for Model-free Task Adaptation in Safety-constrained Grid Environments
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  6. 2024Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
  7. 2026Graph-Based Safe Reinforcement Learning for Multi-Agent Systems with Time-Varying Topology ↗
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