Reinforcement learning (RL) can be subdivided into two fundamental problems: learning and planning. When combined with a simple alpha-beta search, our program also outperformed all traditional (pre-Monte-Carlo) search and machine learning programs on the 9×9 Computer Go Server. Without any explicit search tree, our approach outperformed an unenhanced Monte-Carlo tree search with the same number of simulations. We apply temporal-difference search to the game of 9×9 Go, using a million binary features matching simple patterns of stones. Like Monte-Carlo tree search, the value function is updated from simulated experience but like temporal-difference learning, it uses value function approximation and bootstrapping to efficiently generalise between related states.
Our method, temporal-difference search, combines temporal-difference learning with simulation-based search.
We introduce a new approach to high-performance search in Markov decision processes and two-player games. The key idea is to use the mean outcome of simulated episodes of experience to evaluate each state in a search tree. Monte-Carlo tree search is a recent algorithm for high-performance search, which has been used to achieve master-level play in Go.
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The key idea is to update a value function from episodes of real experience, by bootstrapping from future value estimates, and using value function approximation to generalise between related states. Temporal-difference learning is one of the most successful and broadly applied solutions to the reinforcement learning problem it has been used to achieve master-level play in chess, checkers and backgammon.
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