Submodular functions can be efficiently minimized and are consequently heavily applied in machine learning. There are many cases, however, in which we do not know the function we aim to optimize, but rather have access to training data that is used to learn it.

Based on the reconstruction of the users' travel time distribution, the model is

Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination.

Neural Information Processing Systems http://nips.cc/

In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames.

One of the drawbacks of INs is scaling with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce V AIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that V AIN is effective for multi-agent predictive modeling.

Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI.

Neural Information Processing Systems http://nips.cc/

We demonstrate our approach on the task of manipulating objects with a robotic arm.

We study online reinforcement learning in average-reward stochastic games (SGs).