Monte Carlo Tree Search

How to:

1. $ julia -i main.jl
2. demo() to run AI battle on 3x3 TicTacToe, three-in-a-row version

Quick doc:
function demo(;size=3, inrow=3, time=(1, 1), one=nothing, two=nothing)

• size - board size
• inrow - winning condition
• time - tuple with time for player1 and player2 respectively
• one - player "one" AI agent
• two - player "two" AI agent

Agent(width::Int; explrate=1, rave=0.1) (Agent is basically an AI)

• width - amount of legal moves (for empty 3x3 TicTacToe it is 9)
• explrate - exploration rate
• rave - MC-RAVE optimization parameter

Other possible launches:

• demo(size=5, inrow=4)
• demo(time=(0.3, 0.3))
• ai1 = Agent(9, rave=0.2); mcts(ai1, Game(3), seconds=60); demo(one=ai1);