Search Results for « Multiagent Reinforcement Learning »

This paper presents an approach for defining, solving, and implementing dynamic cooperative maneuver problems in autonomous driving applications. The formulation of these problems considers a set of cooperating cars as part of a multiagent system. A reinforcement learning technique is applied to find a suboptimal policy. The key role in the presented approach is a multiagent maneuvering environment that allows for the simulation of car-like agents within an obstacle-constrained space. Each of the agents is tasked with reaching an individual goal, defined as a specific location in space. The policy is determined during the reinforcement learning process to reach a predetermined goal position for each of the simulated cars. In the experiments, three road scenarios—zipper, bottleneck, and crossroads—were used. The trained policy has been successful in solving the cooperation problem in all scenarios and the positive effects of applying shared rewards between agents have been presented and studied. The results obtained in this work provide a window of opportunity for various automotive applications.

This review provides a comprehensive analysis of cooperative Multi-Agent Reinforcement Learning (MARL) approaches for robotic systems, with particular emphasis on methodological foundations, practical implementations, and emerging challenges. We first examine the evolution of distributed intelligence in robotics, tracing its development from early architectures to modern learning-based frameworks. Our analysis focuses on two complementary paradigms: cooperative methods utilizing Centralized Training with Decentralized Execution (CTDE), and hierarchical approaches that address complexity through temporal and task decomposition. We systematically compare actor-critic methods, value-based approaches, and hierarchical frameworks across theoretical foundations, implementation characteristics, and application domains spanning aerial, ground, and maritime robotics. Our comparative analysis reveals important trade-offs between expressiveness, computational efficiency, and implementation complexity, highlighting that method selection must align with specific application requirements. Furthermore, we identify critical challenges, including the sim-to-real gap, scalability constraints, communication limitations, safety verification, and coordination in heterogeneous teams, mapping promising research directions to address these barriers to widespread deployment. This survey bridges theoretical understanding with practical implementation, providing a structured framework for researchers and practitioners working on multi-agent learning for advanced robotic systems.