# Goal-Driven Autonomous Exploration Through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2103.07119 · HTML: https://mlsanity.com/paper/2103.07119/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2021.

## Built on by (most cited first, 30 of 35)

- [Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks](https://mlsanity.com/paper/2503.21696/) (2025)
- [KiGRAS: Kinematic-Driven Generative Model for Realistic Agent Simulation](https://mlsanity.com/paper/2407.12940/) (2024)
- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping](https://mlsanity.com/paper/2209.14271/) (2022)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments](https://mlsanity.com/paper/2204.03140/) (2022)
- [Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments](https://mlsanity.com/paper/2410.14616/) (2024)
- [BEVNav: Robot Autonomous Navigation Via Spatial-Temporal Contrastive Learning in Bird's-Eye View](https://mlsanity.com/paper/2409.01646/) (2024)
- [Reinforcement Learning with Frontier-Based Exploration via Autonomous Environment](https://mlsanity.com/paper/2307.07296/) (2023)
- [Avoidance Navigation Based on Offline Pre-Training Reinforcement Learning](https://mlsanity.com/paper/2308.01551/) (2023)
- [MeSA-DRL: Memory-Enhanced Deep Reinforcement Learning for Advanced Socially Aware Robot Navigation in Crowded Environments](https://mlsanity.com/paper/2404.05203/) (2024)
- [Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation](https://mlsanity.com/paper/2503.01146/) (2025)
- [CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration](https://mlsanity.com/paper/2503.14254/) (2025)
- [End-to-End Crop Row Navigation via LiDAR-Based Deep Reinforcement Learning](https://mlsanity.com/paper/2509.18608/) (2025)
- [Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation](https://mlsanity.com/paper/2512.24651/) (2025)
- [DREAM: Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems](https://mlsanity.com/paper/2309.17433/) (2023)
- [Learn Once Plan Arbitrarily (LOPA): Attention-Enhanced Deep Reinforcement Learning Method for Global Path Planning](https://mlsanity.com/paper/2401.04145/) (2024)
- [A Digital Twin Framework for Reinforcement Learning with Real-Time Self-Improvement via Human Assistive Teleoperation](https://mlsanity.com/paper/2406.00732/) (2024)
- [Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots](https://mlsanity.com/paper/2410.06372/) (2024)
- [Beyond Visibility Limits: A DRL-Based Navigation Strategy for Unexpected Obstacles](https://mlsanity.com/paper/2503.01127/) (2025)
- [NavG: Risk-Aware Navigation in Crowded Environments Based on Reinforcement Learning with Guidance Points](https://mlsanity.com/paper/2503.02111/) (2025)
- [Mapping at First Sense: A Lightweight Neural Network-Based Indoor Structures Prediction Method for Robot Autonomous Exploration](https://mlsanity.com/paper/2504.04061/) (2025)
- [A Graph-Based Reinforcement Learning Approach with Frontier Potential Based Reward for Safe Cluttered Environment Exploration](https://mlsanity.com/paper/2504.11907/) (2025)
- [SafeMove-RL: A Certifiable Reinforcement Learning Framework for Dynamic Motion Constraints in Trajectory Planning](https://mlsanity.com/paper/2505.12648/) (2025)
- [Hybrid DQN-TD3 Reinforcement Learning for Autonomous Navigation in Dynamic Environments](https://mlsanity.com/paper/2510.26646/) (2025)
- [Platform-Agnostic Reinforcement Learning Framework for Safe Exploration of Cluttered Environments with Graph Attention](https://mlsanity.com/paper/2511.15358/) (2025)
- [Dual-Agent Reinforcement Learning for Adaptive and Cost-Aware Visual-Inertial Odometry](https://mlsanity.com/paper/2511.21083/) (2025)
- [MUOT_3M: A 3 Million Frame Multimodal Underwater Benchmark and the MUTrack Tracking Method](https://mlsanity.com/paper/2602.18006/) (2026)
- [Path-conditioned Reinforcement Learning-based Local Planning for Long-Range Navigation](https://mlsanity.com/paper/2603.13888/) (2026)
- [Massive Parallel Deep Reinforcement Learning for Active SLAM](https://mlsanity.com/paper/2603.25834/) (2026)

## Abstract (arXiv)

In this paper, we present an autonomous navigation system for goal-driven exploration of unknown environments through deep reinforcement learning (DRL). Points of interest (POI) for possible navigation directions are obtained from the environment and an optimal waypoint is selected, based on the available data. Following the waypoints, the robot is guided towards the global goal and the local optimum problem of reactive navigation is mitigated. Then, a motion policy for local navigation is learned through a DRL framework in a simulation. We develop a navigation system where this learned policy is integrated into a motion planning stack as the local navigation layer to move the robot between waypoints towards a global goal. The fully autonomous navigation is performed without any prior knowledge while a map is recorded as the robot moves through the environment. Experiments show that the proposed method has an advantage over similar exploration methods, without reliance on a map or prior information in complex static as well as dynamic environments.