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Python Reinforcement Learning Projects

You're reading from  Python Reinforcement Learning Projects

Product type Book
Published in Sep 2018
Publisher Packt
ISBN-13 9781788991612
Pages 296 pages
Edition 1st Edition
Languages
Authors (3):
Sean Saito Sean Saito
Profile icon Sean Saito
Yang Wenzhuo Yang Wenzhuo
Profile icon Yang Wenzhuo
Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Profile icon Rajalingappaa Shanmugamani
View More author details

Monte Carlo tree search


In games such as Go and chess, players have perfect information, meaning they have access to the full game state (the board and the positions of the pieces). Moreover, there lacks an element of chance that can affect the game state; only the players' decisions can affect the board. Such games are also referred to as perfect-information games. In perfect-information games, it is theoretically possible to enumerate all possible game states. As discussed earlier, this would look such as a tree, where each child node (a game state) is a possible outcome of the parent. In two-player games, alternating levels of this tree represent moves produced by the two competitors. Finding the best possible move for a given state is simply a matter of traversing the tree and finding which sequence of moves leads to a win. We can also store the value, or the expected outcome or reward (a win or a loss) of a given state, at each node.

However, constructing a perfect tree is impractical...

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