Post by steadyeddie on Dec 18, 2016 6:25:32 GMT -8
I use reinforced learning in my rollout policy in one and many player games. But for puzzles it turns out to be very important.
Like everyone else, I found suduko hard. Even once I employ the missing signal hack, I was finding that I'd play a poor move and be lost. So I figured what I wanted to do is encourage my player to play the moves it thought were good during a rollout, in order to influence which moves got better scores in the tree.
So during a rollout I select the move with the best score 16 times more often than the move I think is the weakest.
Importantly, as the results for those rollouts come in, I apply them back to the policy. For many puzzles this accentuates the path to the correct solution.
Like everyone else, I found suduko hard. Even once I employ the missing signal hack, I was finding that I'd play a poor move and be lost. So I figured what I wanted to do is encourage my player to play the moves it thought were good during a rollout, in order to influence which moves got better scores in the tree.
So during a rollout I select the move with the best score 16 times more often than the move I think is the weakest.
Importantly, as the results for those rollouts come in, I apply them back to the policy. For many puzzles this accentuates the path to the correct solution.
