围棋AI，基于蒙特卡洛树搜索(MCTS)算法 Java版.zip

A very simple C++11 Templated MCTS (Monte Carlo Tree Search) implementation with examples for openFrameworks. MCTS Code Based on the Java (Simon Lucas - University of Essex) and Python (Peter Cowling, Ed Powley, Daniel Whitehouse - University of York) impelementations at http://mcts.ai/code/index.html The code is not tailored for any specific use case. It's very generic (but perhaps not generic enough?) It's probably not very optimized, my priority was on readability and flexibility (I wrote it to understand MCTS). It supports variable number of agents, and variable number of actions per step, and is templated. See examples for how to design your 'State' and 'Action' classes. # Usage State state; // contains the current state, it must comply with the State interface Action action; // contains an action that can be applied to a State, and bring it to a new State UCT<State, Action> uct; // Templated class. Builds a partial decision tree and searches it with UCT MCTS // OPTIONAL init uct params uct.uct_k = sqrt(2); uct.max_millis = 0; uct.max_iterations = 100; uct.simulation_depth = 5; loop { // run uct mcts on current state and get best action action = uct.run(state); // apply the action to the current state state.apply_action(action); } # Dependencies: ## Library itself None (C++11) ## Examples openFrameworks 0.9.0 should work on any platform, but project files currently exist only for Windows / VS2015