mlsanityData to 7 Oct 2026

Robot path planning using deep reinforcement learning

arXiv 2302.09120 ↗First posted 17 Feb 2023cs.RO cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Autonomous navigation is challenging for mobile robots, especially in an unknown environment. Commonly, the robot requires multiple sensors to map the environment, locate itself, and make a plan to reach the target. However, reinforcement learning methods offer an alternative to map-free navigation tasks by learning the optimal actions to take. In this article, deep reinforcement learning agents are implemented using variants of the deep Q networks method, the D3QN and rainbow algorithms, for both the obstacle avoidance and the goal-oriented navigation task. The agents are trained and evaluated in a simulated environment. Furthermore, an analysis of the changes in the behaviour and performance of the agents caused by modifications in the reward function is conducted.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 26 of its 52 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

26 of 52 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  6. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  7. 2019Dota 2 with Large Scale Deep Reinforcement Learningcited by 1,084
  8. 2015Prioritized Experience Replaycited by 1,070
  9. 2017Rainbow: Combining Improvements in Deep Reinforcement Learningcited by 774
  10. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  11. 2017A Distributional Perspective on Reinforcement Learningcited by 743
  12. 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 351
14 more
  1. 2019Reinforcement Learning in Healthcare: A Surveycited by 308
  2. 2017Noisy Networks for Explorationcited by 262
  3. 2018Applications of Deep Reinforcement Learning in Communications and Networking: A Surveycited by 163
  4. 2018Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learningcited by 162
  5. 2018End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learningcited by 35
  6. 2021Survey on reinforcement learning for language processingcited by 31
  7. 2021Reinforcement Learning for Quantitative Tradingcited by 24
  8. 2017Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learningcited by 20
  9. 2018FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learningcited by 10
  10. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehiclescited by 10
  11. 2021A Survey on Deep Reinforcement Learning for Data Processing and Analyticscited by 10
  12. 2019Using RGB Image as Visual Input for Mapless Robot Navigationcited by 5
  13. 2021Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learningcited by 4
  14. 2013Control Strategies for Mobile Robot With Obstacle Avoidancecited by 1

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcarecited by 2