# The Emergence of Deep Reinforcement Learning for Path Planning

arXiv: https://arxiv.org/abs/2507.15469 · HTML: https://mlsanity.com/paper/2507.15469/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 16 of 16)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Meta Reinforcement Learning for Sim-to-real Domain Adaptation](https://mlsanity.com/paper/1909.12906/) (2019)
- [Theta*: Any-Angle Path Planning on Grids](https://mlsanity.com/paper/1401.3843/) (2014)
- [TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer](https://mlsanity.com/paper/1907.11200/) (2019)
- [Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.10268/) (2020)
- [UAV Coverage Path Planning under Varying Power Constraints using Deep Reinforcement Learning](https://mlsanity.com/paper/2003.02609/) (2020)
- [Benchmarking Domain Randomisation for Visual Sim-to-Real Transfer](https://mlsanity.com/paper/2011.07112/) (2020)
- [UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2007.00544/) (2020)
- [A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem](https://mlsanity.com/paper/2206.03185/) (2022)
- [Multi-Agent Deep Reinforcement Learning with Human Strategies](https://mlsanity.com/paper/1806.04562/) (2018)
- [Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems](https://mlsanity.com/paper/2103.15053/) (2021)
- [Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments](https://mlsanity.com/paper/2303.17655/) (2023)
- [Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents](https://mlsanity.com/paper/2505.11708/) (2025)
- [A Human Mixed Strategy Approach to Deep Reinforcement Learning](https://mlsanity.com/paper/1804.01874/) (2018)
- [Probabilistic motion planning for non-Euclidean and multi-vehicle problems](https://mlsanity.com/paper/2108.03191/) (2021)

## Built on by (most cited first, 3 of 3)

- [Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation](https://mlsanity.com/paper/2603.13888/) (2026)
- [Q-SpiRL: Quantum Spiking Reinforcement Learning for Adaptive Robot Navigation](https://mlsanity.com/paper/2605.20801/) (2026)
- [FlexPath: Adapting Learned Connectivity Guidance to Path Preferences](https://mlsanity.com/paper/2606.10167/) (2026)

## 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.