Rather than directly determining a policy from high-dimensional observation data, it is better to extract essential information hidden in the observation data and then decide according to it. This is because similar problems can be regarded as the same and the autonomous robots can get the ability to easily adapt to a wide variety of problems. In this research, we are developing new methods based on a variational autoencoder to extract such information (i.

Multi-agent systems with autonomous robots are suitable for dealing with large-scale and complex problems. However, many traditional frameworks have a centralized system that requires all the information of the entire system somehow, and are not scalable. In this research, we propose bottom-up multi-agent reinforcement learning in which autonomous robots understand and cooperate with each other through minimal mutual communication in a decentralized manner. Among them, we are working on the following problems, for example.