Instead of losing the Monte Carlo Tree after decision, store it in memory. Expansions are now much faster and we can re-use existing reward values in a given state.
Instead of losing the Monte Carlo Tree after decision, store it in memory. Expansions are now much faster and we can re-use existing reward values in a given state.