mlsanityData to 14 Oct 2026

Bridging Deep Reinforcement Learning and Motion Planning for Model-Free Navigation in Cluttered Environments

arXiv 2504.07283 ↗First posted 9 Apr 2025cs.ROcited by 1

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Deep Reinforcement Learning (DRL) has emerged as a powerful model-free paradigm for learning optimal policies. However, in navigation tasks with cluttered environments, DRL methods often suffer from insufficient exploration, especially under sparse rewards or complex dynamics with system disturbances. To address this challenge, we bridge general graph-based motion planning with DRL, enabling agents to explore cluttered spaces more effectively and achieve desired navigation performance. Specifically, we design a dense reward function grounded in a graph structure that spans the entire state space. This graph provides rich guidance, steering the agent toward optimal strategies. We validate our approach in challenging environments, demonstrating substantial improvements in exploration efficiency and task success rates.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 34 of its 51 references that are in the map. First posted on arXiv 9 Apr 2025.

Builds on

34 of 51 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning24 Jul

Other references · 33

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  5. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  6. 2018Exploration by Random Network Distillationcited by 993
  7. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  8. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  9. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 639
  10. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  11. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  12. 2020Learning to Explore using Active Neural SLAMcited by 316
21 more
  1. 2016#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learningcited by 309
  2. 2018Learning to Drive in a Daycited by 273
  3. 2020First return, then explorecited by 266
  4. 2017Cognitive Mapping and Planning for Visual Navigationcited by 212
  5. 2019Go-Explore: a New Approach for Hard-Exploration Problemscited by 212
  6. 2017Emergence of Locomotion Behaviours in Rich Environmentscited by 165
  7. 2021Learning High-Speed Flight in the Wildcited by 128
  8. 2018Motion Planning Networkscited by 80
  9. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 77
  10. 2017PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planningcited by 69
  11. 2020Motion Planning and Control for Mobile Robot Navigation Using Machine Learning: a Surveycited by 58
  12. 2021Modular Deep Reinforcement Learning for Continuous Motion Planning with Temporal Logiccited by 39
  13. 2024LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Plannercited by 32
  14. 2022Learning Dynamics and Generalization in Reinforcement Learningcited by 14
  15. 2023Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedbackcited by 13
  16. 2022Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments from Temporal Logic Specificationscited by 12
  17. 2023Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good Statescited by 10
  18. 2024Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Explorationcited by 5
  19. 2022Verified Compositions of Neural Network Controllers for Temporal Logic Control Objectivescited by 4
  20. 2024Task-oriented Robotic Manipulation with Vision Language Modelscited by 4
  21. 2021Robust Motion Planning in the Presence of Estimation Uncertaintycited by 1

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Towards Generalizable Safety in Crowd Navigation via Conformal Uncertainty Handlingcited by 6

Similar, not cited

Close in wording, no citation either way

  1. 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
  2. 2025GRATE: a Graph transformer-based deep Reinforcement learning Approach for Time-efficient autonomous robot Exploration
  3. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  4. 2024Deep Reinforcement Learning-based Large-scale Robot Exploration
  5. 2025A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration
  6. 2025Socially aware navigation for mobile robots: a survey on deep reinforcement learning approaches
  7. 2020World Model as a Graph: Learning Latent Landmarks for Planning
  8. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning