Deep Reinforcement Learning For Autonomous Robot Navigation

By Editorial Team · October 5, 2026 · 4 min read

Deep Reinforcement Learning For Autonomous Robot Navigation Information Guide

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Autonomous Multi-View Navigation via Deep Reinforcement Learning
Autonomous Multi-View Navigation via Deep Reinforcement Learning
Deep Reinforcement Learning for Autonomous Navigation in Dynamic Environments - CASE 2020
Deep Reinforcement Learning for Autonomous Navigation in Dynamic Environments - CASE 2020
Deep Q Reinforcement Learning for Autonomous Navigation of Surgical Snake Robot in Confined Spaces
Deep Q Reinforcement Learning for Autonomous Navigation of Surgical Snake Robot in Confined Spaces
Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning - ArXiv:24
Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning - ArXiv:24
Decentralized Multi-agent Collision Avoidance with Deep Reinforcement Learning
Decentralized Multi-agent Collision Avoidance with Deep Reinforcement Learning
Virtual-to-real Deep Reinforcement Learning: Mobile Robots Mapless Navigation
Virtual-to-real Deep Reinforcement Learning: Mobile Robots Mapless Navigation
Deep Reinforcement learning for real autonomous mobile robot navigation
Deep Reinforcement learning for real autonomous mobile robot navigation
Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning - ArXiv:24
Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning - ArXiv:24
Deep Reinforcement Learning for Autonomous Navigation in Robotic Wheelchairs
Deep Reinforcement Learning for Autonomous Navigation in Robotic Wheelchairs
Deep Reinforcement Learning Methods for Autonomous Driving Safety and Interactivity
Deep Reinforcement Learning Methods for Autonomous Driving Safety and Interactivity
Robot Navigation of Environments with Unknown Rough Terrain Using Deep Reinforcement Learning
Robot Navigation of Environments with Unknown Rough Terrain Using Deep Reinforcement Learning

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Last Updated: October 5, 2026

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Summary

Mickyas Tamiru Asfaw, David Bertoin, Valentin Guillet develop a Bruno Fournier and Sébastien Biner develop and evaluate This is the supplementary video. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), August 2020 Hong Kong. This video introduces the development of a Original paper: arxiv.org/abs/2407.18962 Title: (IROS 2017) arxiv: arxiv.org/abs/1703.00420 A continuous control policy is trained via the Soft Actor Critic (SAC) algorithm for autonomously navigating a differential drive Ph.D. thesis defense of Xiaobai Ma. Slides available at web.stanford.edu/group/sisl/public/defense_ma.pdf. K. Zhang, F. Niroui, M. Ficocelli and G. Nejat, “

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