# Research on Autonomous Robots Navigation based on Reinforcement Learning

arXiv: https://arxiv.org/abs/2407.02539 · HTML: https://mlsanity.com/paper/2407.02539/

## Key facts

- Cited by 12 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 2 of its 13 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## 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, 2 of 2)

- [Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey](https://mlsanity.com/paper/2001.02319/) (2020)
- [Pedestrian Dominance Modeling for Socially-Aware Robot Navigation](https://mlsanity.com/paper/1810.06613/) (2018)

## Built on by (most cited first, 12 of 12)

- [Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics](https://mlsanity.com/paper/2411.13587/) (2024)
- [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024)
- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)
- [Comprehensive Overview of Artificial Intelligence Applications in Modern Industries](https://mlsanity.com/paper/2409.13059/) (2024)
- [Deep Learning for Lung Disease Classification Using Transfer Learning and a Customized CNN Architecture with Attention](https://mlsanity.com/paper/2408.13180/) (2024)
- [Analysis and Design of a Personalized Recommendation System Based on a Dynamic User Interest Model](https://mlsanity.com/paper/2410.09923/) (2024)
- [Enhancing Skin Lesion Diagnosis with Ensemble Learning](https://mlsanity.com/paper/2409.04381/) (2024)
- [EdgeNavMamba: Mamba Optimized Object Detection for Energy Efficient Edge Devices](https://mlsanity.com/paper/2510.14946/) (2025)
- [Angel or Demon: Investigating the Plasticity Interventions' Impact on Backdoor Threats in Deep Reinforcement Learning](https://mlsanity.com/paper/2605.14587/) (2026)
- [Taxonomy and Trends in Reinforcement Learning for Robotics and Control Systems: A Structured Review](https://mlsanity.com/paper/2510.21758/) (2025)
- [Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2511.23148/) (2025)
- [LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map](https://mlsanity.com/paper/2605.16899/) (2026)

## Abstract (arXiv)

Reinforcement learning continuously optimizes decision-making based on real-time feedback reward signals through continuous interaction with the environment, demonstrating strong adaptive and self-learning capabilities. In recent years, it has become one of the key methods to achieve autonomous navigation of robots. In this work, an autonomous robot navigation method based on reinforcement learning is introduced. We use the Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models to optimize the path planning and decision-making process through the continuous interaction between the robot and the environment, and the reward signals with real-time feedback. By combining the Q-value function with the deep neural network, deep Q network can handle high-dimensional state space, so as to realize path planning in complex environments. Proximal policy optimization is a strategy gradient-based method, which enables robots to explore and utilize environmental information more efficiently by optimizing policy functions. These methods not only improve the robot's navigation ability in the unknown environment, but also enhance its adaptive and self-learning capabilities. Through multiple training and simulation experiments, we have verified the effectiveness and robustness of these models in various complex scenarios.