Goal Driven Autonomous Exploration Through Deep Reinforcement Learning

By themelower On Apr 5, 2026

Goal Driven Autonomous Exploration Through Deep Reinforcement Learning Abstract: in this letter, we present an autonomous navigation system for goal driven exploration of unknown environments through deep reinforcement learning (drl). 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.

Goal Driven Autonomous Exploration Through Deep Reinforcement Pdf A goal driven autonomous exploration and mapping system that combines reactive and planned robot navigation. first, a navigation policy is learned through a deep reinforcement learning framework in a simulated environment. Abstract in this letter, we present an autonomous navigation system for goal driven exploration of unknown environments through deep reinforcement learning (drl). The topic of robots exploring and mapping the environment has been prevalent in these years. in this project, we focused on the navigation system in daily scenarios and aimed to validate the effectiveness of the method in paper “goal driven autonomous exploration through deep reinforcement learning”. This article addresses the application of deep reinforcement learning methods in the context of local navigation, i.e., a robot moves toward a goal location in unknown and cluttered workspaces equipped only with limited range exteroceptive sensors by means of reward shaping in actor–critic networks.

Goal Driven Autonomous Mapping Through Deep Reinforcement Learning And The topic of robots exploring and mapping the environment has been prevalent in these years. in this project, we focused on the navigation system in daily scenarios and aimed to validate the effectiveness of the method in paper “goal driven autonomous exploration through deep reinforcement learning”. This article addresses the application of deep reinforcement learning methods in the context of local navigation, i.e., a robot moves toward a goal location in unknown and cluttered workspaces equipped only with limited range exteroceptive sensors by means of reward shaping in actor–critic networks. Humans can use exploration system for navigation to a global goal, without their best knowledge of their surroundings and instincts to the necessity of human control or prior information about locate possible pathways to the goal, even if working in the environment. Goal driven autonomous exploration through deep reinforcement learning free download as pdf file (.pdf), text file (.txt) or read online for free. J. inst. control, robot. syst. 26 (3), 168 176, 2020. View recent discussion. abstract: 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.

Goal Driven Autonomous Exploration Through Deep Reinforcement Learning

Goal Driven Autonomous Exploration Through Deep Reinforcement Learning Humans can use exploration system for navigation to a global goal, without their best knowledge of their surroundings and instincts to the necessity of human control or prior information about locate possible pathways to the goal, even if working in the environment. Goal driven autonomous exploration through deep reinforcement learning free download as pdf file (.pdf), text file (.txt) or read online for free. J. inst. control, robot. syst. 26 (3), 168 176, 2020. View recent discussion. abstract: 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.

Goal Driven Autonomous Exploration Through Deep Reinforcement Learning

Goal Driven Autonomous Exploration Through Deep Reinforcement Learning J. inst. control, robot. syst. 26 (3), 168 176, 2020. View recent discussion. abstract: 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.

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Goal Driven Autonomous Mapping Through Deep Reinforcement Learning and Planning-Based Navigation

Goal Driven Autonomous Mapping Through Deep Reinforcement Learning and Planning-Based Navigation

Goal Driven Autonomous Mapping Through Deep Reinforcement Learning and Planning-Based Navigation Goal Driven Autonomous Mapping additional experiments Multi-Rover Guidance & Navigation for Planetary Exploration with Deep Reinforcement Learning Active SLAM for Autonomous Exploration using Deep Reinforcement Learning (Real-World) Active SLAM for Autonomous Exploration using Deep Reinforcement Learning (Simulation) Information-Based Heuristics for Learned Goal-Driven Exploration and Mapping How to train autonomous robots Goal-Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space Goal Oriented Obstacle Avoidance with Deep Reinforcement Learning in Continuous Action Space Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep RL Autonomous 2D exploration with Deep Reinforcement Learning Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning - ArXiv:24 Robot multi-point patrolling with Deep Reinforcement Learning Long term robot navigation with Deep Reinforcement Learning Autonomous Robot Exploration with Deep Reinforcement Learning Autonomous Multi-View Navigation via Deep Reinforcement Learning Deep Reinforcement Learning-based Obstacle Avoidance in Continuous Navigation Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning - ArXiv:24 Deep Reinforcement Learning Supervised Autonomous Exploration in Office Environments

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