Abstract We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent
architecture that brings insights from predictive state representations to reinforcement …

We study reinforcement learning for partially observable multi-agent systems where each
agent only has access to its own observation and reward and aims to maximize its …

Mobile robot navigation in a dynamic environment with pedestrians is essential for service
robots operating in a living environment. Accordingly, the robot needs to understand and …

This paper investigates the problem of Traffic Signal Control (TSC) in large-scale road
networks. In extensive road networks, it is customary to define each intersection as an agent …

State estimation and tracking (also known as filtering) is an integral part of any system
performing inference in a partially observable environment, whether it is a robot that is …

Intuitively a dynamical system is any observable quantity which changes over time
according to some fixed rule. Building models to understand, predict, and control dynamical …

In the traditional framework of spectral learning of stochastic time series models, model
parameters are estimated based on trajectories of fully recorded observations. However …

This thesis focuses on moment and kernel-based methods for applications in Robotics and
Natural Language Processing. Kernel and moment-based learning leverage information …