pit.py

import Arena
from MCTS import MCTS


from tictactoe.TicTacToeGame import TicTacToeGame, display
from tictactoe.TicTacToePlayers import *
from tictactoe.tensorflow.NNet import NNetWrapper as NNet

from gobang.GobangGame import GobangGame, display as display1
from gobang.GobangPlayers import *
from gobang.tensorflow.NNet import NNetWrapper as NNet1


from othello.OthelloGame import OthelloGame, display as display2
from othello.OthelloPlayers import *
from othello.tensorflow.NNet import NNetWrapper as NNet2

from connect4.Connect4Game import Connect4Game, display as display3
from connect4.Connect4Players import *
from connect4.tensorflow.NNet import NNetWrapper as NNet3

import numpy as np
from utils import *

"""
use this script to play any two agents against each other, or play manually with
any agent.
"""
choice = "othello"

if choice == "tictactoe":
    g = TicTacToeGame(3)
    n1 = NNet(g)
    n1.load_checkpoint('./temp/', 'best75_eps95_dim3.pth.tar')
    gamename = "tictactoe"
    display = display
    hp = HumanTicTacToePlayer(g).play
if choice == "gobang":
    g = GobangGame(5, 4)
    n1 = NNet1(g)
    n1.load_checkpoint('./temp/', 'curent13temp_iter75_eps350_dim5.pth.tar')
    gamename = "gobang"
    display = display1
    hp = HumanGobangPlayer(g).play
if choice == "othello":
    g = OthelloGame(8)
    n1 = NNet2(g)
    n1.load_checkpoint('./temp/', 'curent14temp:iter14:eps200:dim8.pth.tar')
    gamename = "othello"
    display = display2
    hp = MinMaxOthelloPlayer(g,3).play
if choice == "connect4":
    g = Connect4Game(5, 6)
    n1 = NNet3(g)
    n1.load_checkpoint('./temp/','best75_eps300_dim5.pth.tar')
    gamename = "connect4"
    display = display3
    hp = HumanConnect4Player(g).play

# all players
#rp = RandomPlayer(g).play
#gp = GreedyOthelloPlayer(g).play


# nnet players
args1 = dotdict({'numMCTSSims': 400, 'cpuct': 1.5, 'epsilon': 0, 'dirAlpha': 0.3})
mcts1 = MCTS(g, n1, args1)
n1p = lambda x: np.argmax(mcts1.getActionProb(x, temp=0))

'''
n2 = NNet2(g)
n2.load_checkpoint('./temp/', 'AlphaZerocurent20temp:iter20:eps140:dim6.pth.tar')
args2 = dotdict({'numMCTSSims': 500, 'cpuct': 1.0, 'epsilon': 0, 'dirAlpha': 0.3})
mcts2 = MCTS(g, n2, args2)
n2p = lambda x: np.argmax(mcts2.getActionProb(x, temp=0))
'''

arena = Arena.Arena(n1p, hp, g, mcts1,  display=display, evaluate=True, name=gamename)
print(arena.playGames(4, verbose=True))