Man In The Table (Information Set) Monte Carlo Tree Search

This library is hopefully an easy-to-understand implementation of Information Set Monte Carlo Tree search written in Python.

The mascot of this project is the Turk:

Features

An easy-to-understand simple implementation of MCTS/ISMCTS so others can see how it works
Aid board game designers by giving them information about their games that make the game match their criteria of a fun game e.g.:
- tree depth report - how many moves on average does a game take to complete - is my game too long or short?
- custom evaluation functions so other metrics can be tracked

Implement a class with the following classmethods:

initial_state() - returns the initial state of a game
apply_move(state, move) - returns a copy of state with the move applied - you are responsible for ensuring that if there is structural sharing it is done in a way that mutable changes to game state don't accidentally affect other nodes (when in doubt deepcopy the whole state - if you want to optimize for memory you only need to copy parts of the state that changed)
get_moves(state) - return a tuple of (is_random (randomly select a move each time, for most games this is False), list of moves (selected move is passed back into apply_move))
get_winner(state) - returns None if no one won, the player that won or Draw if there was a tie
current_player(state) - returns the current player

Optional methods:

print_board(state) - prints the board for the given state
determine(state) - if this is defined it randomly selects possible moves a player could play given their play history (so in a trick taking game if they haven't followed a particular suit when it was lead then they can't possibly have that suit - see the Euchre example in the tests directory)

More aids for board game designers:
- lead changes - presumably the more often this happens the more engaged players are
- is there a dominant strategy that always seems to win or do a variety of strategies win? (could be determined by counting occurrences of certain moves or abilities that players collected in leaf nodes)
- should a particular ability be nerfed or made non-exclusive (i.e. if players end up with it do they win most games)?
- branching factor report - can help with analysis paralysis detection - does my game offer too many choices to players on every turn?