Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey (2408.09675v1)

Abstract: Reinforcement Learning (RL) is a potent tool for sequential decision-making and has achieved performance surpassing human capabilities across many challenging real-world tasks. As the extension of RL in the multi-agent system domain, multi-agent RL (MARL) not only need to learn the control policy but also requires consideration regarding interactions with all other agents in the environment, mutual influences among different system components, and the distribution of computational resources. This augments the complexity of algorithmic design and poses higher requirements on computational resources. Simultaneously, simulators are crucial to obtain realistic data, which is the fundamentals of RL. In this paper, we first propose a series of metrics of simulators and summarize the features of existing benchmarks. Second, to ease comprehension, we recall the foundational knowledge and then synthesize the recently advanced studies of MARL-related autonomous driving and intelligent transportation systems. Specifically, we examine their environmental modeling, state representation, perception units, and algorithm design. Conclusively, we discuss open challenges as well as prospects and opportunities. We hope this paper can help the researchers integrate MARL technologies and trigger more insightful ideas toward the intelligent and autonomous driving.

• The paper surveys multi-agent reinforcement learning for autonomous driving, reviewing methods, challenges, benchmarks, safety, and future research directions.
• The survey proposes a structured framework for evaluating autonomous driving benchmarks like simulators and datasets based on criteria including realism, scalability, and diversity.
• The paper examines safety guarantees in MARL for autonomous driving, suggesting methods like control barrier functions, and addresses the sim-to-real gap and potential solutions.

Multi-Agent Reinforcement Learning for Autonomous Driving: Insights from a Comprehensive Survey

The paper "Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey," provides an exhaustive overview of the intersection of multi-agent reinforcement learning (MARL) techniques and the domain of autonomous driving. Authored by Ruiqi Zhang et al., the paper explores the complexity introduced by the multi-agent nature of real-world traffic systems and evaluates the appropriateness of various MARL methodologies for such purposes.

Key Contributions

The authors offer a significant contribution by introducing a structured framework to assess autonomous driving benchmarks, notably simulators and datasets. This framework evaluates these resources based on key attributes: realism, scalability, diversity, efficiency, transferability, and support infrastructure. Within this context, comprehensive assessments of state-of-the-art simulators, such as CARLA, SMARTS, MetaDrive, and others, are discussed, highlighting their respective strengths and current applications in MARL for autonomous driving.

MARL Methods and Challenges

The paper organizes MARL methodologies within two core paradigms: centralized training with decentralized execution (CTDE) and decentralized training and execution (DTDE). It offers insights into the challenges inherent in deploying these methods, such as non-stationarity, partial observability, credit assignment, and scalability. The CTDE paradigm is presented as a viable solution to address partial observability, leveraging a central critic to streamline learning across a multi-agent setup. In parallel, decentralized strategies, which employ independent learning agents, are put forward as a solution to scalability issues, though they introduce non-stationarity challenges.

Advanced value decomposition methods and recent research innovations like independent policy optimization (IPO) are explored to improve MARL's adaptability and performance. These methodologies aim to enhance the system's overall efficiency and agent-specific learning, offering tangible pathways forward for integrating MARL in practical autonomous driving applications.

Safety and Generalization Concerns

Safety guarantees within MARL are thoroughly examined, with particular attention given to soft and probabilistic assurances. The authors advocate for stronger guarantees through control barrier functions (CBFs) and related strategies to manage state-wise constraints effectively. The paper also discusses the limitation of existing frameworks in transferring simulation-trained policies to real-world systems, a knowledge gap identified as the "sim-to-real gap." Contributions from model-based RL, improved state representations, and offline data integration are suggested as potential avenues for bridging these gaps.

Future Directions and Reflections

The survey identifies several promising directions for future research. The development of realistic, large-scale datasets to support offline MARL learning is highlighted as crucial for advancing the field. Additionally, human-in-the-loop frameworks and advancements in LLMs offer new opportunities to enhance algorithm explainability and decision-making robustness.

In summation, this paper lays a strong foundational understanding of the current MARL landscape for autonomous driving and provides a trajectory for future research. While the field is still grappling with bridging theoretical advancements and practical deployment, insights from this survey will guide researchers in overcoming existing hurdles and advancing the capabilities of autonomous driving technologies further. Future efforts will likely focus on integrating MARL more deeply into real-world scenarios, assuring safety, scalability, and reliability within the context of increasingly complex urban environments.