Asynchronous Advantage Actor-Critic Agent for Starcraft II
  Deep reinforcement learning, and especially the Asynchronous Advantage
Actor-Critic algorithm, has been successfully used to achieve super-human
performance in a variety of video games. Starcraft II is a new challenge for
the reinforcement learning community with the release of pysc2 learning
environment proposed by Google Deepmind and Blizzard Entertainment. Despite
being a target for several AI developers, few have achieved human level
performance. In this project we explain the complexities of this environment
and discuss the results from our experiments on the environment. We have
compared various architectures and have proved that transfer learning can be an
effective paradigm in reinforcement learning research for complex scenarios
requiring skill transfer.
