Online Learning and Planning of Dynamical Systems Using Gaussian Processes

Abstract

Decision-making problems with complicated and/or partially unknown underlying generative process and limited data has been quite pervasive in several research areas including robotics, automatic control, operations research, artificial intelligence, economics, medicine etc. In such areas, we can take great advantage from algorithms that learn from data and aid decision making. Over years, Reinforcement learning (RL) has been emerged as a general computational framework to the goal-directed experience-based learning for sequential decision making under uncertainty. However, with no task-specific knowledge, it often lacks efficiency in terms of the number of required samples. This lack of sample efficiency makes RL inapplicable to many real world problems. Thus, a central challenge in RL is how to extract more information from available experience to facilitate fast learning with little data. The contribution of this dissertation are: ? Proposal of (online) sequential (or non-episodic) reinforcement learning frame- work for modeling a variety of single agent problems and algorithms. ? Systematic treatment of model bias for sample efficiency by using Gaussian processes for model learning and using the uncertainty information for long term prediction in the planning algorithms. ? Empirical evaluation of the results for the swing-up control of simple pendulum and designing suitable (interrupted) drug strategies for HIV infected patient.