Deep Reinforcement Learning for Autonomous Driving: A Survey

Analysis

Why This Paper Matters

Deep reinforcement learning (DRL) has emerged as a promising approach for autonomous driving, enabling agents to learn complex policies directly from high-dimensional sensory inputs. However, the field lacks a unified overview of how DRL can be applied across the diverse tasks involved in automated driving. This survey by Kiran et al. fills that gap by systematically reviewing DRL algorithms and providing a taxonomy of driving tasks where DRL has been employed. Given the rapid growth of both DRL and autonomous driving research, such a structured synthesis is essential for researchers and practitioners seeking to navigate the landscape.

The paper also addresses critical computational challenges in deploying DRL agents in real-world driving scenarios, such as sample efficiency, safety, and robustness. By delineating adjacent domains like behavior cloning, imitation learning, and inverse reinforcement learning, it clarifies the boundaries and relationships between these related but distinct approaches. This helps practitioners choose appropriate methods for specific problems. Furthermore, the discussion on simulators and validation methods highlights the practical aspects of training and testing DRL agents, which are often overlooked in algorithm-centric reviews.

With over 2,300 citations, this survey has become a key reference in the intersection of reinforcement learning and autonomous driving. Its comprehensive coverage and practical focus make it valuable for both newcomers and experts. The paper's impact is evident in the numerous subsequent works that build upon its taxonomy and identified challenges.

Technical Contributions

• Comprehensive review of DRL algorithms: The paper summarizes key DRL algorithms, including value-based, policy gradient, and actor-critic methods, and discusses their applicability to autonomous driving.
• Taxonomy of automated driving tasks: It categorizes driving tasks such as lane keeping, overtaking, intersection handling, and end-to-end driving, mapping them to DRL approaches used in the literature.
• Deployment challenges: The survey identifies computational challenges in real-world deployment, including sample complexity, safety guarantees, and sim-to-real transfer.
• Adjacent learning paradigms: It distinguishes DRL from behavior cloning, imitation learning, and inverse reinforcement learning, providing clarity on their roles and relationships.
• Simulation and validation: The paper discusses the use of simulators for training DRL agents and methods for validating, testing, and robustifying solutions, emphasizing the importance of realistic simulation environments.

Results

As a survey, this paper does not present new experimental results. Instead, it synthesizes findings from existing literature, providing a structured overview of the state of the art. The authors compile and compare various DRL applications in autonomous driving, highlighting successful implementations and common pitfalls. They also discuss evaluation metrics and benchmarks used in the field, though no new metrics are introduced. The paper's main "result" is the organization and critical analysis of the literature, which serves as a foundation for future research.

Significance

The significance of this survey lies in its role as a foundational reference for the application of DRL in autonomous driving. By providing a clear taxonomy and identifying key challenges, it guides researchers toward promising directions and helps practitioners avoid common pitfalls. The discussion on simulators and validation methods is particularly relevant for bridging the gap between simulation and real-world deployment. Moreover, the delineation of adjacent learning paradigms fosters a deeper understanding of how DRL fits into the broader machine learning landscape for autonomous systems. Overall, this paper has significantly influenced the research agenda in DRL for autonomous driving and continues to be widely cited, underscoring its lasting impact on the field.