This project is a master thesis by a group on
the 10th semester of the software education
at Aalborg University.
The topic of this project surrounds using
reinforcement- and transfer-learning on the
complex environment of Starcraft II. We test
a number of dierent agent architectures to
find a candidate best suited for applying
transfer learning.
To test if transfer is possible on Starcraft II,
we use a network architecture proposed by
Google DeepMind in 2016 called progressive
networks, which allows us to leverage
knowledge from multiple tasks when training
on new tasks. At the same time progressive
networks do not suer from catastrophic
forgetting, which allows us to approximate
how much transfer is happening and where
in the network it is occurring.