Mobile Networks for Computer Go
This work addresses performance optimization for Go-playing AI engines, but it appears incremental as it builds on existing methods like Alpha Zero.
The paper tackled the problem of improving neural network architectures for computer Go by evaluating Mobile Networks with modified policy and value heads, resulting in assessments of accuracy, efficiency, and playing strength.
The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines. For example the use of residual networks gave a 600 ELO increase in the strength of Alpha Go. This paper proposes to evaluate the interest of Mobile Network for the game of Go using supervised learning as well as the use of a policy head and a value head different from the Alpha Zero heads. The accuracy of the policy, the mean squared error of the value, the efficiency of the networks with the number of parameters, the playing speed and strength of the trained networks are evaluated.