Chess Minimax

I've decided to continue my adventure after minimax implementation for tic tac toe.

Implemented

• board state evaluation based on pieces weights (pretty base solution)
• minimax search algorithm for best move/optional depth
• alpha-beta search tree pruning
• game class for games with different players
• game result stats

Problems

• Bots don't know how to end a game, so it almost always ends in a draw. Possible solution: endgame tablebase.
• Minimax for depth > 4 execution takes forever even with alpha-beta pruning and move sorting by pieces importance. Possible solution: tree caching, better heuristic, parallelization (oh that's hard), build tree only for some promising moves (for example in some range from the opponent).

TODO:

• player class & methods for human play
• game loop for random player & human player
• simple func for game generations and saving result stats
• add minimax method for simple eval by pieces weigths
• different weights for board eval -> stats bot vs bot
• alpha-beta pruning
• too long - sort possible moves by pieces: more weight at first -> better pruning
• caching (???)

Inspiration:

• https://www.saturncloud.io/published/lksfr/programming-a-chess-player/chess/Programming%20a%20Chess%20Player.ipynb
• https://en.wikipedia.org/wiki/Chess_strategy