Multi-Agent Reinforcement Learning and Smart Mobility
Safe Route Optimisation for Multiple Vessels
Our laboratory investigates route planning algorithms for multiple vessels using distributional reinforcement learning (DRL). Specifically, by combining DRL with the Obstacle Zone by Target (OZT) proposed by the National Maritime Research Institute, we have developed a method that enables vessels to learn actions that avoid collision risks based on predictions of vessel motion, thereby supporting safe navigation. Our future goals are to develop algorithms that can be applied to more advanced simulations and to construct learning algorithms that take into account real-world effects that cannot be ignored, such as wind.
Route Optimisation for Multiple Aerial Delivery Robots
Japan's working-age population has continued to decline in recent years. In the transportation sector, where the impact is particularly severe, securing labour through the introduction of robots and autonomous driving technologies has become an urgent issue. In collaboration with Assistant Professor Ding at Kyoto University, Mr Aoyama at Panasonic Holdings Corporation, and Associate Professor Hayashi at Okayama University, our laboratory investigates route optimisation algorithms for aerial delivery drones. Specifically, we train drone policies in simulations that reproduce dynamic factors in aerial delivery, such as route blockages and control instability caused by wind, and develop methods that enable safe transportation even in real-world environments. Our laboratory also jointly organises the Delivery Robot Routing Problems Challenge (DRP Challenge) every year to explore better algorithms.