Dynamic games feature a state-space complexity that scales superlinearly with the number of players. This makes this class of games often intractable even for a handful of players. We introduce the factorization process of dynamic games as a transformation leveraging the independence of players at equilibrium to build a leaner game graph. When applicable, it yields fewer nodes, fewer players per game node, hence much faster solutions. While for the general case checking for independence of players requires to solve the game itself, we observe that for dynamic games in the robotic domain there exist exact heuristics based on the spatio-temporal occupancy of the individual players. We validate our findings in realistic autonomous driving scenarios showing that already for a 4-player intersection we have a reduction of game nodes and solving time close to 99%.

In recent years, mobility on demand has experienced a major revival due to various ride-hailing companies entering the market. Competing in this field requires an efficient operation. Therefore, the applied policy, which cares for vehicle-to-customer assignment and vehicle repositioning, has to achieve good customer service and minimize cost while trying to keep the impact on the environment as low as possible. A promising approach is to coordinate the control of the entire fleet, which is foreseen to become even easier with the possibility of autonomous vehicles in mind. Anticipating future demand requires a good understanding of the spatiotemporal distributions of request origins and destinations, and the resulting imbalance between vehicle demand and availability. This results from a multitude of topological, demographic, and social effects, which are almost impossible to sufficiently capture in a handcrafted model of reasonable complexity. This can be circumvented by leveraging machine learning approaches. In this paper, an image-like representation of the city and its fleet's state is introduced. It is comprehensive and intuitive to use as input to convolutional neural networks, which in the past have already been proven to capture spatial relationships very well. This allows operating on realistic, full-sized traffic networks without greatly increasing the number of parameters the neural network has to learn and, hence, keeps the training effort low. Additionally, this state is combined with a similarly constructed repositioning action, reflecting a 2D distribution of a well-performing operational policy. This approach allows replacement of complex, handcrafted mathematical models by a single, compact, auto-encoder-like neural network.

Typically, to enlarge the operating domain of an object detector, more labeled training data is required. We describe a method called wormhole learning, which allows to extend the operating domain without additional data, but only with temporary access to an auxiliary sensor with certain invariance properties. We describe the instantiation of this principle with a regular visible-light RGB camera as the main sensor, and an infrared sensor as the temporary sensor. We start with a pre-trained RGB detector; then we train the infrared detector based on the RGB-inferred labels; finally we re-train the RGB detector based on the infrared-inferred labels. After these two transfer-learning steps, the RGB detector has enlarged its operating domain by inheriting part of the invariance to illumination of the infrared sensor; in particular, the RGB detector is now able to see much better at night. We analyze the wormhole learning phenomenon by bounding the possible gain in accuracy using mutual information properties of the two sensors and considered operating domain.