Asynchronous Advantage Actor-Critic Agent for Starcraft II
This work addresses a specific problem for AI developers in video game AI, but it is incremental as it builds on existing methods like Asynchronous Advantage Actor-Critic.
The paper tackles the challenge of achieving human-level performance in Starcraft II using deep reinforcement learning, demonstrating that transfer learning is effective for skill transfer in complex scenarios.
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.