Learning from oracle demonstrations-A new approach to develop autonomous intersection management control algorithms based on multiagent deep reinforcement learning
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This work was supported in part by the MCIN/AEI/10.13039/501100011033 under Grant PID2020-116329GB-C22; and in part by the Fundación Séneca, Región de Murcia, Spain, under Grant 20740/FPI/18.Fecha de publicación
2022-05-16Editorial
IEEECita bibliográfica
A. Guillen-Perez and M. -D. Cano, "Learning From Oracle Demonstrations—A New Approach to Develop Autonomous Intersection Management Control Algorithms Based on Multiagent Deep Reinforcement Learning," in IEEE Access, vol. 10, pp. 53601-53613, 2022, doi: 10.1109/ACCESS.2022.3175493Revisión por pares
siPalabras clave
Autonomous intersection managementIntelligent transport systems
Intersection traffic management
Learning from demonstrations
Multi-agent deep reinforcement learning
Resumen
Worldwide, many companies are working towards safe and innovative control systems for
Autonomous Vehicles (AVs). A key component is Autonomous Intersection Management (AIM) systems,
which operate at the level of traffic intersections and manage the right-of-way for AVs, thereby improving
flow and safety. AIM traditionally uses control policies based on simple rules. However, Deep Reinforcement
Learning (DRL) can provide advanced control policies with the advantage of proactively reacting and
forecasting hazardous situations. The main drawback of DRL is the training time, which is fast in simple tasks
but not negligible when addressing real-world problems with multiple agents. Learning from Demonstrations (LfD) emerged to solve this problem, significantly speeding up training, and reducing the exploration
problem. The challenge is that LfD requires an expert to extract new demonstrations. Therefore, in this paper,
we propose the use of an agent, previously trained by imitation ...
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