Dynamic Network Architectures for Deep Q-Learning: Modelling Neurogenesis in Artificial Intelligence
Typ
Examensarbete för masterexamen
Program
Publicerad
2019
Författare
Eriksson, Pontus
Westlund Gotby, Love
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Artificial neural networks have become popular within a range of machine learning
fields for their ability to solve complex problems, with one of the uses as function
approximators in Q-learning. These networks generally have static architectures,
which is a problem in the regard of artificial general intelligence, since no single
specific architecture is optimal for all problems. In this thesis, we implement and
evaluate a proof of concept for a novel approach of a dynamic network architecture,
resulting in a model that can be seen as a combination of compressed classical
table-based Q-learning and artificial neural networks. The model presented performs
true tabula rasa deep Q-learning, starting with an empty network that is gradually
extended with nodes when experiencing “surprising” events, and is capable of generalization
by abstracting important features from noisy input. Finally, we show that
the model can learn from delayed rewards in simple environments and compare it
with the well-established DQN algorithm.
Beskrivning
Ämne/nyckelord
dynamic neural network , artificial neural network , ANN , Q-learning , DQN , deep learning , machine learning , reinforcement learning