Combining online and offline knowledge in UCT

The UCT algorithm learns a value function online using sample-based search. The TD() algorithm can learn a value function offline for the on-policy distribution. We consider three approaches for combining offline and online value functions in the UCT algorithm. First, the offline value function is used as a default policy during Monte-Carlo simulation. Second, the UCT value function is combined with a rapid online estimate of action values. Third, the offline value function is used as prior knowledge in the UCT search tree. We evaluate these algorithms in 9 ? 9 Go against GnuGo 3.7.10. The first algorithm performs better than UCT with a random simulation policy, but surprisingly, worse than UCT with a weaker, handcrafted simulation policy. The second algorithm outperforms UCT altogether. The third algorithm outperforms UCT with handcrafted prior knowledge. We combine these algorithms in MoGo, the world's strongest 9 ? 9 Go program. Each technique significantly improves MoGo'...

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Sylvain Gelly, David Silver
• Keywords: ICML 2007, Machine Learning, Offline Value Function, UCT Algorithm, UCT Value Function