The Emergence of Deep Reinforcement Learning for Path Planning

TL;DR

This paper reviews traditional and deep reinforcement learning methods for path planning in autonomous systems, highlighting recent advances, hybrid approaches, and future research challenges.

Contribution

It provides a comprehensive survey of DRL-based path planning, categorizing algorithms, analyzing their strengths and limitations, and discussing hybrid methods and future directions.

Findings

DRL offers adaptable and scalable path planning solutions.

Hybrid approaches combine classical and learning-based methods effectively.

Identifies key challenges and promising research avenues in DRL for navigation.

Abstract

The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with their environments. This survey provides a comprehensive overview of traditional approaches as well as the recent advancements in DRL applied to path planning tasks, focusing on autonomous vehicles, drones, and robotic platforms. Key algorithms across both conventional and learning-based paradigms are categorized, with their innovations and practical implementations highlighted. This is followed by a thorough discussion of their respective strengths and limitations in terms of computational efficiency, scalability, adaptability, and robustness. The survey concludes by identifying key open challenges and outlining promising avenues for future research. Special attention is given to hybrid approaches that integrate DRL with classical planning techniques to leverage the benefits of both learning-based adaptability and deterministic reliability, offering promising directions for robust and resilient autonomous navigation.