mlsanityData to 14 Oct 2026

Platform-Agnostic Reinforcement Learning Framework for Safe Exploration of Cluttered Environments with Graph Attention

arXiv 2511.15358 ↗First posted 19 Nov 2025cs.ROcited by 0

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

Abstract (arXiv)

Autonomous exploration of obstacle-rich spaces requires strategies that ensure efficiency while guaranteeing safety against collisions with obstacles. This paper investigates a novel platform-agnostic reinforcement learning framework that integrates a graph neural network-based policy for next-waypoint selection, with a safety filter ensuring safe mobility. Specifically, the neural network is trained using reinforcement learning through the Proximal Policy Optimization (PPO) algorithm to maximize exploration efficiency while minimizing safety filter interventions. Henceforth, when the policy proposes an infeasible action, the safety filter overrides it with the closest feasible alternative, ensuring consistent system behavior. In addition, this paper introduces a reward function shaped by a potential field that accounts for both the agent's proximity to unexplored regions and the expected information gain from reaching them. The proposed framework combines the adaptability of reinforcement learning-based exploration policies with the reliability provided by explicit safety mechanisms. This feature plays a key role in enabling the deployment of learning-based policies on robotic platforms operating in real-world environments. Extensive evaluations in both simulations and experiments performed in a lab environment demonstrate that the approach achieves efficient and safe exploration in cluttered spaces.

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 20 of its 35 references that are in the map. First posted on arXiv 19 Nov 2025.

Builds on

20 of 35 references.

Closest ancestors · 1

Same direction, newest first

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

Other references · 19

  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
7 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

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  3. 2026Graph-Based Safe Reinforcement Learning for Multi-Agent Systems with Time-Varying Topology ↗
  4. 2025Leveraging Analytic Gradients in Provably Safe Reinforcement Learning
  5. 2026DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles
  6. 2024Deep Reinforcement Learning with Enhanced PPO for Safe Mobile Robot Navigation
  7. 2024Safe Policy Exploration Improvement via Subgoals
  8. 2025Bridging Deep Reinforcement Learning and Motion Planning for Model-Free Navigation in Cluttered Environments