Leveraging A* Pathfinding for Efficient Deep Reinforcement Learning in Obstacle-Dense Environments

This study introduces a novel approach to integrate A* pathfinding algorithms with deep reinforcement learning (Deep RL) for navigating vehicles in environments laden with obstacles, without requiring pre-existing map information during execution. Our method demonstrates enhanced data efficiency and improved success rates in target tracking and collision avoidance, even in unfamiliar terrains.

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Published
2024-12-18
DOI
https://doi.org/10.1109/icmla61862.2024.00012
Primary Topic
VLSI and FPGA Design Techniques
Type
article
Field-Weighted Citation Impact
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article

Leveraging A* Pathfinding for Efficient Deep Reinforcement Learning in Obstacle-Dense Environments

Antoine Fagette, Charles Dansereau, Junior Samuel López-Yépez, Filipe Carvalhais-Sanches
VLSI and FPGA Design Techniques
article

Leveraging A* Pathfinding for Efficient Deep Reinforcement Learning in Obstacle-Dense Environments

Antoine Fagette, Charles Dansereau, Junior Samuel López-Yépez, Filipe Carvalhais-Sanches
article en

Abstract

This study introduces a novel approach to integrate A* pathfinding algorithms with deep reinforcement learning (Deep RL) for navigating vehicles in environments laden with obstacles, without requiring pre-existing map information during execution. Our method demonstrates enhanced data efficiency and improved success rates in target tracking and collision avoidance, even in unfamiliar terrains.

Thales (Canada) (CA)
Openalex Percentile: Top 74%
VLSI and FPGA Design Techniques
DOISourceOpenAlex
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Leveraging A* Pathfinding for Efficient Deep Reinforcement Learning in Obstacle-Dense Environments — Antoine Fagette, Charles Dansereau, et al. · (2024) | TGRS Research Map | TGRS