Robotic agent control combining reactive and learning capabilities

This paper presents the concept of an autonomous robotic agent combining reactive and machine learning-based algorithms. The focus is on the machine learning-based part that we implement by neural networks. A method for reducing the environment state space to a smaller conceptual world space is given. We then show how the concept of "lifelong learning" can be implemented by neural networks in a robotic action planner.