  Applying reinforcement learning to robotic systems poses a number of
challenging problems. A key requirement is the ability to handle continuous
state and action spaces while remaining within a limited time and resource
budget. Additionally, for safe operation, the system must make robust decisions
under hard constraints. To address these challenges, we propose a model based
approach that combines Gaussian Process regression and Receding Horizon
Control. Using sparse spectrum Gaussian Processes, we extend previous work by
updating the dynamics model incrementally from a stream of sensory data. This
results in an agent that can learn and plan in real-time under non-linear
constraints. We test our approach on a cart pole swing-up environment and
demonstrate the benefits of online learning on an autonomous racing task. The
environment's dynamics are learned from limited training data and can be reused
in new task instances without retraining.
