Autonomous navigation of mobile robots in unstructured, dynamic environments is a critical challenge in robotics. Deep Reinforcement Learning (DRL) has emerged as a promising approach for enabling robots to learn complex navigation policies through continuous interaction with their surroundings. This review paper presents a value-based algorithms, policy-based algorithms, hybrid DRL algorithms, hierarchical DRL algorithms, and multi-agent DRL (MADRL), with a focus on their applicability to real-world robotic navigation tasks. Despite significant advancements, several challenges remain such as sample inefficiency, safety constraints, and limited generalization to unseen environments. This review further highlights open research issues and future directions, including sim-to-real transfer, multi-agent collaboration, and hybrid approaches that integrate DRL with classical navigation methods.
ZouQSunQChenL, et al. A comparative analysis of lidar SLAM-based indoor navigation for autonomous vehicles. IEEE Trans Intell Transp Syst2022; 23(7): 6907–6921.
2.
HoangVDJoKH. Path planning for autonomous vehicle based on heuristic searching using online images. Vietnam J Comput Sci2015; 2(2): 109–120.
3.
XuXZuoLLiX, et al. A reinforcement learning approach to autonomous decision making of intelligent vehicles on highways. IEEE Trans Syst Man Cybern Syst2019; 50(10): 1–14.
4.
ChenPPeiJLuW, et al. A deep reinforcement learning based method for real-time path planning and dynamic obstacle avoidance. Neurocomputing2022; 497: 64–75.
5.
AlaghaAMizouniRSinghS, et al. Adaptive target localization under uncertainty using multi-agent deep reinforcement learning with knowledge transfer. Internet of Things2025; 29: 101447.
6.
FuBChenLZhouY, et al. An improved A* algorithm for the industrial robot path planning with high success rate and short length. Robot Auton Syst2018; 106: 26–37.
7.
SunYFangMSuY. AGV path planning based on improved Dijkstra algorithm. J Phys Conf Ser2021; 1746(1): 012052.
8.
YanFLiuYSXiaoJZ. Path planning in complex 3D environments using a probabilistic roadmap method. Int J Autom Comput2013; 10(6): 525–533.
9.
SongXFangHJiaoX, et al. Autonomous mobile robot navigation using machine learning. In: 2012 IEEE 6th international conference on information and automation for sustainability, 27 September 2012, pp.135–140. IEEE.
HiroseNSadeghianAGoebelP, et al. To go or not to go? a near unsupervised learning approach for robot navigation. arXiv preprint arXiv:1709.05439, 2017.
12.
StraussCSahinF. Autonomous navigation based on a Q-learning algorithm for a robot in a real environment. In: 2008 IEEE international conference on system of systems engineering, 2 June 2008, pp.1–5. IEEE.
13.
BansalPJoshiJHansS, et al. Autonomous navigation of unmanned aerial vehicle using reinforcement learning. In: KumarASinghS (eds). Sustainable mobility: policies, challenges and advancements. John Wiley & Sons, Inc., 2024, pp.79–103.
14.
QiuXWanKLiF. Autonomous robot navigation in dynamic environment using deep reinforcement learning. In: 2019 IEEE 2nd international conference on automation, electronics and electrical engineering (AUTEEE), 22 November 2019, pp.338–342. IEEE.
15.
Escobar-NaranjoJCaizaGAyalaP, et al. Autonomous navigation of robots: optimization with DQN. Appl Sci2023; 13(12): 7202.
16.
SivayaziKMannayeeG. Modeling and simulation of a double DQN algorithm for dynamic obstacle avoidance in autonomous vehicle navigation. e-Prime Adv Electr Eng Electron Energy2024; 8: 100581.
17.
WenSLvXLamHK, et al. Probability dueling DQN active visual SLAM for autonomous navigation in indoor environment. Ind Robot Int J Robot Res Appl2021; 48(3): 359–365.
18.
SchaulTQuanJAntonoglouI, et al. Prioritized experience replay. arXiv preprint arXiv:1511.05952, 2015.
19.
HassaniHNikanSShamiA. Traffic navigation via reinforcement learning with episodic-guided prioritized experience replay. Eng Appl Artif Intell2024; 137: 109147.
20.
RongcaiZHongweiXKexinY. Autonomous collision avoidance system in a multi-ship environment based on proximal policy optimization method. Ocean Eng2023; 272: 113779.
21.
ParkMLeeSYHongJS, et al. Deep deterministic policy gradient-based autonomous driving for mobile robots in sparse reward environments. Sensors2022; 22(24): 9574.
22.
NeamahHAMayorgaOA. Optimized TD3 algorithm for robust autonomous navigation in crowded and dynamic human-interaction environments. Results Eng2024; 24: 102874.
23.
ElallidBBenamarNBagaaM, et al. Enhancing autonomous driving navigation using soft actor-critic. Future Internet2024; 16(7): 238.
24.
ToghiBValienteRSadighD, et al. Altruistic maneuver planning for cooperative autonomous vehicles using multi-agent advantage actor-critic. arXiv preprint arXiv:2107.05664, 2021.
25.
MoghaddasiKRajabiSGharehchopoghFS. An enhanced asynchronous advantage actor-critic-based algorithm for performance optimization in mobile edge computing -enabled internet of vehicles networks. Peer Peer Netw Appl2024; 17(3): 1169–1189.
26.
DingWLiSQianH, et al. Hierarchical reinforcement learning framework towards multi-agent navigation. In: 2018 IEEE international conference on robotics and biomimetics (ROBIO), 12 December 2018, pp.237–242. IEEE.
27.
LiCWangLHuangZ. Hindsight-aware deep reinforcement learning algorithm for multi-agent systems. Int J Mach Learn Cybern2022; 13(7): 2045–2057.
28.
YuanZLiYDuanJ. Water surface autonomus navigation simulation via multi-agent proximal policy optimization. In: 2024 international conference on intelligent robotics and automatic control (IRAC), 29 November 2024, pp.525–531. IEEE.
29.
TobinJFongRRayA, et al. Domain randomization for transferring deep neural networks from simulation to the real world. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), 24 September 2017, pp.23–30. IEEE.
30.
WenSWenZZhangD, et al. A multi-robot path-planning algorithm for autonomous navigation using meta-reinforcement learning based on transfer learning. Appl Soft Comput2021; 110: 107605.
31.
ZhouZRenJZengZ, et al. A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments. CAAI Trans Intell Technol2023; 8: 1–16.
32.
WenLDuanJLiSE, et al. Safe reinforcement learning for autonomous vehicles through parallel constrained policy optimization. In: 2020 IEEE 23rd international conference on intelligent transportation systems (ITSC), 20 September 2020, pp.1–7. IEEE.
33.
YurtseverECapitoLRedmillK, et al. Integrating deep reinforcement learning with model-based path planners for automated driving. In: 2020 IEEE intelligent vehicles symposium (IV), 19 October 2020, pp.1311–1316. IEEE.
34.
JaafraYDeruyverALaurentJL, et al. Context-aware autonomous driving using meta-reinforcement learning. In: 2019 18th IEEE international conference on machine learning and applications (ICMLA), 16 December 2019, pp.450–455. IEEE.
35.
HeLAoufNSongB. Explainable Deep Reinforcement Learning for UAV autonomous path planning. Aerosp Sci Technol2021; 118: 107052.
36.
ZhangSLiYDongQ. Autonomous navigation of UAV in multi-obstacle environments based on a deep reinforcement learning approach. Appl Soft Comput2022; 115: 108194.
37.
XieZDamesP. Drl-vo: learning to navigate through crowded dynamic scenes using velocity obstacles. IEEE Trans Robot2023; 39(4): 2700–2719.
38.
HuKChenZKangH, et al. 3D vision technologies for a self-developed structural external crack damage recognition robot. Autom Constr2024; 159: 105262.
39.
XingXDingHLiangZ, et al. Robot path planner based on deep reinforcement learning and the seeker optimization algorithm. Mechatronics2022; 88: 102918.
40.
ZhouXGaoYGuanL. Towards goal-directed navigation through combining learning based global and local planners. Sensors2019; 19(1): 176.
41.
MirandaVRFRezendeAMCRochaTL, et al. Autonomous navigation system for a delivery drone. J Contr Autom Electr Syst2022; 33(1): 141–155.
42.
BinbinXJizhanLMengH, et al. Research progress on autonomous navigation technology of agricultural robot. In: 2021 IEEE 11th annual international conference on CYBER technology in automation, control, and intelligent systems (CYBER)27 July 2021, pp.891–898. IEEE.
43.
RutkowskiAJBarnesJESmithAT. Path planning for optimal cooperative navigation. In: 2016 IEEE/ION position, location and navigation symposium (PLANS), 11 April 2016, pp.359–365. IEEE.
44.
YasinJNMahboobHHaghbayanMH, et al. Energy-efficient navigation of an autonomous swarm with adaptive consciousness. Remote Sens2021; 13(6): 1059.
45.
NamSWooCKangS, et al. SLAM-DRLnav: a SLAM-enhanced deep reinforcement learning navigation framework for indoor self-driving. In: 2023 international conference on mechatronics, control and robotics (ICMCR), 18 February 2023, pp.44–48. IEEE.
46.
JooKJBaeSHPyoJW, et al. Autonomous navigation with active SLAM for disinfecting robot. In: 2022 22nd international conference on control, automation and systems (ICCAS), 27 November 2022, pp.112–116. IEEE.
47.
StormontDPAllanVH. Managing risk in disaster scenarios with autonomous robots. Journal of Systemics, Cybernetics and Informatics2009; 7(08): 66–71.
48.
ZhuWRajuAShamsahA, et al. Emobipednav: emotion-aware social navigation for bipedal robots with deep reinforcement learning. IEEE/ASME Trans Mechatron2026; 1–13. DOI: https://doi.org/10.1109/TMECH.2026.3653909
49.
FlögelDFischerLRudolfT, et al. Socially integrated navigation: a social acting robot with deep reinforcement learning. In: 2024 IEEE/RSJ international conference on intelligent robots and systems (IROS), 14 October 2024, pp.4785–4792. IEEE.
50.
RizehvandiAAzadiS. Highway decision-making strategy for autonomous vehicle for overtaking maneuver using deep reinforcement learning (DRL) method. Amirkabir Journal of Mechanical Engineering2024; 56(4): 595–620.
51.
KhalilYHMouftahHT. Integration of motion prediction with end-to-end latent RL for self-driving vehicles. In: 2021 international wireless communications and mobile computing (IWCMC), 28 June 2021, pp.1111–1116. IEEE.
52.
SeoDJKimJ. Development of autonomous navigation system for an indoor service robot application. In: 2013 13th international conference on control, automation and systems (ICCAS 2013), 20 October 2013, pp.204–206. IEEE.
53.
ZhangCChenMLinY, et al. Lidar-based autonomous exploration method of mobile robot using deep reinforcement learning in unknown environments. IEEE Trans Instrum Meas2025; 74: 1–16.
54.
ZhaoJGanZLiangJ, et al. Path planning research of a UAV base station searching for disaster victims’ location information based on deep reinforcement learning. Entropy2022; 24(12): 1767.
55.
Calderón-CordovaCSarangoRCastilloD, et al. A deep reinforcement learning framework for control of robotic manipulators in simulated environments. IEEE Access2024; 12: 103133–103161.
