DeepMind achieved StarCraft II GrandMaster Level, but at what cost?
Thanks to the advancement in deep learning and reinforcement learning, DeepMind achieved groundbreaking success first in Atari, then in Go, and finally in Starcraft II.
One of the major benefits of AI research is the ease of replication. Before this decade, many researchers had to hand-code gradient calculations, loss functions, and data processing pipelines. Nowadays, solving a MINST dataset problem using Keras only requires less than 70 lines of code.
Replicating DeepMind’s Atari results is relatively easy. Having an Nvidia 2080 Ti GPU would undoubtedly speed up your training process, but all you need is a modern computer. There are many resources available online, helping us speed up the development, training, and testing phase of Atari Deep RL Agent. Most notably, OpenAI Baselines has already implemented the Atari solution for us. And in fact, this task is easy enough to be designed as a Berkeley CS285 Deep Reinforcement Learning homework (OK. This class is not easy, but you get my point.)
Replicating AlphaGo, DeepMind’s agent for playing Go, is a different story. AlphaGo Zero: Starting from scratch is the official story behind developing Alpha Go Zero and you read can more details regarding the performance and computation requirements in the Mastering the game of Go without…
