![]() An episode is only completed when an outcome can be yielded from it. Complete one episode starting from the chosen child node, where random actions are chosen for future states.win/lose/draw), create at least one child node for that leaf node and choose one child node from those created. If the leaf node does not lead to an outcome to the episode (e.g. ![]() Leaf node in this case refers to a node that has not yet gone through the expansion stage, rather than its traditional definition which is "a node without child nodes". Traverse through the search tree from the root node to a leaf node, while only selecting the most promising child nodes.The final game result of each episode is then used to determine the weight of all nodes traversed during that episode so that the probability of choosing an action that yields higher current and potential rewards is increased. MCTS attempts to identify the most promising moves at each state by choosing random actions at that state for every episode (playouts/rollouts). Player name is automatically paired with state and used as key when searching through transposition table._import() and _export() methods are now referred to as save() and load() respectively.Removed delete_last_action() method from game environment class.tqdm now automatically detects the IDE that is used and outputs the progress bar accordingly.cPickle is now used in place of JSONPickle to speed up the exporting and importing process of MCTS.Different variants of MCTS are split into different files for improved readability of code.Type hints are now supported for better documentation of code.Now your game state can be returned as a list! However, do ensure that typings of both state and action remain constant throughout.Automatic hashing of state using str() which can be reverted using Python's in-built eval(). ![]()
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