Combining online and offline knowledge in UCT
Proceedings of the 24th international conference on Machine learning
Parallel Monte-Carlo Tree Search
CG '08 Proceedings of the 6th international conference on Computers and Games
Amdahl's Law in the Multicore Era
Computer
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Efficient selectivity and backup operators in Monte-Carlo tree search
CG'06 Proceedings of the 5th international conference on Computers and games
Consistency modifications for automatically tuned Monte-Carlo tree search
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Creating an upper-confidence-tree program for havannah
ACG'09 Proceedings of the 12th international conference on Advances in Computer Games
Revisiting Monte-Carlo tree search on a normal form game: NoGo
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Parallel Monte-Carlo tree search for HPC systems
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
The grand challenge of computer Go: Monte Carlo tree search and extensions
Communications of the ACM
Monte-Carlo tree search parallelisation for computer go
Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference
| Hi-index | 0.02 |
Monte-Carlo Tree Search is now a well established algorithm, in games and beyond. We analyze its scalability, and in particular its limitations and the implications in terms of parallelization. We focus on our Go program MoGo and our Havannah program Shakti. We use multicore machines and message-passing machines. For both games and on both type of machines we achieve adequate efficiency for the parallel version. However, in spite of promising results in self-play there are situations for which increasing the time per move does not solve anything. Therefore parallelization is not a solution to all our problems. Nonetheless, for problems where the Monte-Carlo part is less biased than in the game of Go, parallelization should be quite efficient, even without shared memory.