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Deep Reinforcement Learning based Automatic Exploration for Navigation in Unknown Environment

arXiv 2007.11808 ↗First posted 23 Jul 2020cs.ROcited by 29

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Abstract (arXiv)

This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to this problem via stacking decision rules is impossible to cover various environments and sensor properties. Learning based control methods are adaptive for these scenarios. However, these methods are damaged by low learning efficiency and awkward transferability from simulation to reality. In this paper, we construct a general exploration framework via decomposing the exploration process into the decision, planning, and mapping modules, which increases the modularity of the robotic system. Based on this framework, we propose a deep reinforcement learning based decision algorithm which uses a deep neural network to learning exploration strategy from the partial map. The results show that this proposed algorithm has better learning efficiency and adaptability for unknown environments. In addition, we conduct the experiments on the physical robot, and the results suggest that the learned policy can be well transfered from simulation to the real robot.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2020.

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Citing papers per year: 2021: 3; 2022: 11; 2023: 4; 2024: 7; 2025: 3; 2026: 1.

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  1. 2024A Survey on Text-guided 3D Visual Grounding: Elements, Recent Advances, and Future Directionscited by 24
  2. 2022TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Drivingcited by 23
  3. 2021Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillationcited by 15
  4. 2023Deep Learning for Visual Localization and Mapping: A Surveycited by 14
  5. 2022Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualizationcited by 13
  6. 2024Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularizationcited by 11
  7. 2024DARE: Diffusion Policy for Autonomous Robot Explorationcited by 11
  8. 2025Dream to Drive with Predictive Individual World Modelcited by 11
  9. 2022Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Trafficcited by 7
  10. 2025TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learningcited by 7
  11. 2022Deep-Attack over the Deep Reinforcement Learningcited by 5
  12. 2024Sample-efficient Unsupervised Policy Cloning from Ensemble Self-supervised Labeled Videoscited by 4
17 more
  1. 2021Deep Reinforcement Learning-based UAV Navigation and Control: A Soft Actor-Critic with Hindsight Experience Replay Approachcited by 3
  2. 2022Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representationcited by 3
  3. 2023Deep Reinforcement Learning with Explicit Context Representationcited by 3
  4. 2024Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learningcited by 3
  5. 2024A Survey on Reinforcement Learning Applications in SLAMcited by 3
  6. 2021OPT-GAN: A Broad-Spectrum Global Optimizer for Black-box Problems by Learning Distributioncited by 2
  7. 2022Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learningcited by 2
  8. 2025Salience-Invariant Consistent Policy Learning for Generalization in Visual Reinforcement Learningcited by 1
  9. 2022Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximationcited by 0
  10. 2022Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methodscited by 0
  11. 2022A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learningcited by 0
  12. 2022Task-Agnostic Learning to Accomplish New Taskscited by 0
  13. 2022MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigationcited by 0
  14. 2023Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Samplingcited by 0
  15. 2023ComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discoverycited by 0
  16. 2024Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robotscited by 0
  17. 2026VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policycited by 0