Multi-Objective Multi-Agent Reinforcement Learning
Multi-Objective Path Optimisation and Control for Multiple Robots
Solving real-world problems often requires handling multiple tasks, whereas AI systems are typically designed to achieve a single objective. For example, an image recognition AI aims to identify objects in an input image. However, when such recognition is applied to autonomous driving, various other objectives must also be considered, such as collision avoidance and reaching a destination. The same issue arises in multi-agent reinforcement learning.
Our laboratory addresses these limitations and investigates ways to solve real-world problems using multi-objective AI. Specifically, we improve algorithms for multi-objective multi-agent path planning and multi-objective multi-agent reinforcement learning so that they can be trained with a practical amount of learning for real-world applications.