Abstract:
Learning-based methods for sequential decision making, i.e., methods which leverage data, have shown the ability to solve complex problems in recent years. This includes control of dynamical systems, as well as mastering games such as Go and StarCraft. In addition, these methods often promise to be applicable to a wide variety of problems.
A subclass of these methods are model-based methods. They leverage data to learn a model which allows predicting the evolution of a dynamical system to control. In recent research, it was shown that these methods, in contrast to model-free methods, require less data to be trained. In addition, model-based methods allow re-using the dynamics model when the task to be solved has changed, and straightforward adaptation to changes in the system’s dynamics.
One particular focus of this thesis is on learning dynamics models which can data-efficiently adapt to changes in the system’s dynamics, as well as the efficient collection of data to adapt a learned model. In this regard, two novel methods are presented.
In the application domain of autonomous robot navigation, in which both parameters of the robot and the terrain are subject to change, a novel method comprising an adaptive dynamics model is presented and evaluated on a simulated environment.
A further advantage of model-based methods is the ability to incorporate physical prior knowledge for model design. In this thesis, we demonstrate that leveraging physical prior knowledge is advantageous for the task of tracking and predicting the motion of a table tennis ball, respecting its spin.
However, model-based methods, in particular planning with learned models, have to cope with certain challenges. For long prediction horizons, which are required if the effect of an action is apparent only far in the future, model errors accumulate. In addition, model-based planning is commonly computationally intensive, which is problematic if high-frequency, reactive control is required. In this thesis, a method is presented to alleviate these problems. To this end, we propose a two-layered hierarchical method. Model-based planning is only applied on the higher layer on symbolic abstractions. On the lower-layer, model-free reactive control is used. We show successful application of this method to board games which can only be interacted with through a robotic manipulator, e.g., a robotic arm, which requires high-frequency reactive control.
