To complement our existing optimization libraries, we’ve released two additional optimization simulations, A* Search and Monte Carlo Tree Search.
A* search is one of the most popular search algorithms. It uses an optimistic heuristic plus breadth first search to find the best possible route through a graph to a target destination node. Use the A* search library in combination with an agent based model to have agents navigate graphs efficiently.
Monte Carlo Tree Search (MCTS) is a modified tree search that uses heuristics to prioritize searching certain branches of a game tree based on the likelihood of finding winning moves. A Monte Carlo distribution determines the game tree moves, optimizing for the branches that have returned the highest score in previous iterations but balancing that with exploring novel choices. MCTS has had a lot of success in games, most notably serving as the underlying algorithm for AlphaGo. In the simulation, an MCTS behavior powers the search of an agent playing tic-tac-toe.