Description
<div>MonteCarlo Tree Search (MCTS) is a bestfirst search method that does not require a positional evaluation function. Bestfirst search is an instance of general treesearch algorithm in which a node is selected for expansion based on a specific rule. It is based on the randomized exploration of the search space. Instead of building the whole game tree (like treesearch algorithms such as Minimax) or just simply run Monte Carlo simulations from the current position, MCTS builds up the</div><div>tree in an incrementally and asymmetric manner.</div><div><br/></div><div>It consists of four steps, repeated as long as there is time left. They are: selection, expansion, simulation and backpropagation. In the selection step, the tree is traversed from the root node until we reach a leaf node or a node with untried moves E. In the expansion step, we select one child node C and add it to the tree. In the simulation step, we run a selfplaying game from node C until game is ended and we have a result R, which is +1 if player wins, 0 if it is a draw and 1 if player is lost. In the backpropagation step, the result R is propagated backwards through previously traversed nodes. Finally after the running time is over, a child node of the root node is selected based on the number of visits or its value. MCTS is a general problem that can be applied in many areas. The most success application of MCTS is to build Goplaying agents.</div><div><br/></div><div>This bounty is created so that we can propose different implementations of MCTS for different problems (games, scheduling problems...)</div>
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