Multi-Agent Reinforcement Learning for Collaborative Autonomous Vehicle Fleets
Annotatsiya
Multi-agent reinforcement learning (MARL) is now a disruptive technology of autonomous vehicle (AV) fleets, which involves continuous vehicle decisions and coordination of vehicles to operate dynamically. The MARL allows AVs to acquire cooperative behaviors, such as adaptive platooning, intersection navigation, and collision avoidance, without any explicit programming: it uses either decentralized or centralized training regimes. Other techniques, like QMIX, MADDPG, and graph-based RL, may extend the scalability and communication efficiency of the agents relative to traffic uncertainties that arise during execution. Some of the major problems include non-stationarity, partial observability and the need to have reliable communication protocols with low latency. Recent advances unite vehicle-to-everything (V2X) networks and MARL to improve situational awareness and cooperative plan of routes. Research exists like SUMO and CARLA that offers simulated environments that can be utilized to aid mass training and transfer learning that may include sim-to-real bidirectional transfer. Areas of use are logistics (convoy optimization), ridesharing (demand-responsive routing), and smart city (traffic flow control). Open problems involve the issuance of safety guarantees, adversarial agent, and optimal balancing between the goals of a single vessel and the fleet. Hybrid MARL-rule-based structures, energy-efficient coordination and human-AV interaction schemes will be explored in the future. This paradigm is expected to bring revolutionary breakthroughs to transportation systems with higher efficiency, safety, and sustainable development through the intelligent cooperation of fleets.