The Emergence of Deep Reinforcement Learning for Path Planning

2025-07-21

AI Summary

To address the insufficient robustness and adaptability of path planning for autonomous systems—such as autonomous vehicles, UAVs, and robots—in complex dynamic environments, this paper proposes a tightly integrated hybrid paradigm combining deep reinforcement learning (DRL) with classical planning algorithms. The method synergistically unifies deterministic techniques—including graph search, linear programming, and evolutionary computation—with DRL’s online decision-making capability, thereby enhancing environmental adaptability while preserving safety guarantees and interpretability. Through systematic comparative evaluation across computational efficiency, dynamic responsiveness, and interference resilience, we identify fundamental limitations in current approaches concerning generalization, real-time performance, and verifiability. The study further outlines key future research directions for safety-critical applications: hierarchical cooperative learning, simulation-to-reality transfer, and integration of formal verification methods.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Developing intelligent path planning for autonomous systems in dynamic environments
• Comparing traditional and deep reinforcement learning methods for navigation
• Exploring hybrid approaches combining DRL with classical planning techniques

Innovation

• Deep reinforcement learning for path planning
• Hybrid DRL with classical planning techniques
• Learning-based adaptability and deterministic reliability