In March 2016, AlphaGo, a computer Go program developed by Google DeepMind, won a 5-game match against Lee Sedol, one of the best Go players in the world. Its victory marks a major advance in the field of computer Go. However, much remains to be done. There is a gap between the computational power AlphaGo used in the match and the computational power available to the majority of computer users today. Further, the communication between two of the techniques used by AlphaGo, neural networks and Monte Carlo Tree Search, can be improved. We investigate four different approaches towards accomplishing this end, with a focus on methods that require minimal computational power. Each method shows promise and can be developed further.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Perez, Oscar
• Keller, Joshua E

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-042616-172920

Advisor

• Sarkozy, Gabor N

Year

• 2016

Center

• Budapest, Hungary Project Center - MQP

Sponsor

• MTA SZTAKI

Date created

• 2016-04-26

Location

• Budapest

Resource type

• Major Qualifying Project

Major

• Interdisciplinary

Rights statement

• In Copyright