Intrinsic Rewards for Maintenance, Approach, Avoidance and Achievement Goal Types

In reinforcement learning, reward is used to guide the learning process. The reward is often designed to be task-dependent, and it may require significant domain knowledge to design a good reward function. This paper proposes general reward functions for maintenance, approach, avoidance, and achievement goal types. These reward functions exploit the inherent property of each type of goal and are thus task-independent. We also propose metrics to measure an agent's performance for learning each type of goal. We evaluate the intrinsic reward functions in a framework that can autonomously generate goals and learn solutions to those goals using a standard reinforcement learning algorithm. We show empirically how the proposed reward functions lead to learning in a mobile robot application. Finally, using the proposed reward functions as building blocks, we demonstrate how compound reward functions, reward functions to generate sequences of tasks, can be created that allow the mobile robot to learn more complex behaviors

Bootstrapping inverse Kinematics with Goal Babbling

Rolf M, Steil JJ, Gienger M. Bootstrapping inverse Kinematics with Goal Babbling. Presented at the IEEE Int. Conf. on Development and Learning

Reliability of Extreme Learning Machines

Neumann K. Reliability of Extreme Learning Machines. Bielefeld: Bielefeld University Library; 2014.The reliable application of machine learning methods becomes increasingly important in challenging engineering domains. In particular, the application of extreme learning machines (ELM) seems promising because of their apparent simplicity and the capability of very efficient processing of large and high-dimensional data sets. However, the ELM paradigm is based on the concept of single hidden-layer neural networks with randomly initialized and fixed input weights and is thus inherently unreliable. This black-box character usually repels engineers from application in potentially safety critical tasks. The problem becomes even more severe since, in principle, only sparse and noisy data sets can be provided in such domains. The goal of this thesis is therefore to equip the ELM approach with the abilities to perform in a reliable manner. This goal is approached in three aspects by enhancing the robustness of ELMs to initializations, make ELMs able to handle slow changes in the environment (i.e. input drifts), and allow the incorporation of continuous constraints derived from prior knowledge. It is shown in several diverse scenarios that the novel ELM approach proposed in this thesis ensures a safe and reliable application while simultaneously sustaining the full modeling power of data-driven methods