3 accepted papers
Learning control policies using deep reinforcement learning has shown great success for a variety of applications, including robotics and automated driving. A key area limiting the adaptation of RL in the real world is the lack of trust in the decision-making process of such policies. Therefore, exp
The combination of self-play and planning has achieved great successes in sequential games, for instance in Chess and Go. However, adapting algorithms such as AlphaZero to simultaneous games poses a new challenge. In these games, missing information about concurrent actions of other agents is a limi…
Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address the problem of learning deep neural networks on small datasets. Our proposed arch…