MultiMax

State-of-the-Art Game Tree Search for Arbitrary Games

MultiMax is a project which demonstrates general state-of-the-art game tree search algorithms that can be easily implemented for arbitrary games in Python.

Game and Action Interfaces

• Implement the simple Game and Action interfaces to make use of MultiMax's evaluators.
• See examples for an example chess implementation.

Negamax Evaluator

• Fail-soft alpha-beta pruning
• Iterative deepening
• Principal variation search
• Quiescence search

MCTS Evaluator

• Monte Carlo Tree Search (MCTS)
• Upper Confidence Bound 1 applied to trees (UCT)

Caching

• Two-tier evaluation and transposition caching