mlsanityData to 14 Oct 2026

The Emergence of Deep Reinforcement Learning for Path Planning

arXiv 2507.15469 ↗First posted 21 Jul 2025cs.RO cs.AIcited by 3

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

Abstract (arXiv)

The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with their environments. This survey provides a comprehensive overview of traditional approaches as well as the recent advancements in DRL applied to path planning tasks, focusing on autonomous vehicles, drones, and robotic platforms. Key algorithms across both conventional and learning-based paradigms are categorized, with their innovations and practical implementations highlighted. This is followed by a thorough discussion of their respective strengths and limitations in terms of computational efficiency, scalability, adaptability, and robustness. The survey concludes by identifying key open challenges and outlining promising avenues for future research. Special attention is given to hybrid approaches that integrate DRL with classical planning techniques to leverage the benefits of both learning-based adaptability and deterministic reliability, offering promising directions for robust and resilient autonomous navigation.

Key facts. Cited by 3 papers 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 16 of its 74 references that are in the map. First posted on arXiv 21 Jul 2025.

Builds on

16 of 74 references.

References · 16

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2016OpenAI Gymcited by 1,736
  3. 2019Meta Reinforcement Learning for Sim-to-real Domain Adaptationcited by 49
  4. 2014Theta*: Any-Angle Path Planning on Gridscited by 26
  5. 2019TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfercited by 22
  6. 2020Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learningcited by 21
  7. 2020UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learningcited by 15
  8. 2020Benchmarking Domain Randomisation for Visual Sim-to-Real Transfercited by 15
  9. 2020UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approachcited by 8
  10. 2022A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problemcited by 7
  11. 2018Multi-Agent Deep Reinforcement Learning with Human Strategiescited by 5
  12. 2021Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systemscited by 4
4 more
  1. 2023Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environmentscited by 4
  2. 2025Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agentscited by 4
  3. 2018A Human Mixed Strategy Approach to Deep Reinforcement Learningcited by 2
  4. 2021Probabilistic motion planning for non-Euclidean and multi-vehicle problemscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigationcited by 0
  2. 2026Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigationcited by 0
  3. 2026FlexPath: Adapting Learned Connectivity Guidance to Path Preferencescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  2. 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
  3. 2024Deep Reinforcement Learning for Mobile Robot Path Planning
  4. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  5. 2025Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review
  6. 2026Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
  7. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning
  8. 2024Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning