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game_board.py
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game_board.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Dec 7 15:14:24 2018
@author: initial-h
"""
import numpy as np
from collections import deque
from GUI_v1_4 import GUI
class Board(object):
'''
board for the game
'''
def __init__(self, **kwargs):
self.width = int(kwargs.get('width', 11))
self.height = int(kwargs.get('height', 11))
self.states = {}
# board states stored as a dict,
# key: move as location on the board,
# value: player as pieces type
self.n_in_row = int(kwargs.get('n_in_row', 5))
# need how many pieces in a row to win
self.players = [1, 2]
# player1 and player2
self.feature_planes = 8
# how many binary feature planes we use,
# in alphago zero is 17 and the input to the neural network is 19x19x17
# here is a (self.feature_planes+1) x self.width x self.height binary feature planes,
# the self.feature_planes is the number of history features
# the additional plane is the color feature that indicate the current player
# for example, in 11x11 board, is 11x11x9,8 for history features and 1 for current player
self.states_sequence = deque(maxlen=self.feature_planes)
self.states_sequence.extendleft([[-1,-1]] * self.feature_planes)
#use the deque to store last 8 moves
# fill in with [-1,-1] when one game start to indicate no move
def init_board(self, start_player=0):
'''
init the board and set some variables
'''
if self.width < self.n_in_row or self.height < self.n_in_row:
raise Exception('board width and height can not be '
'less than {}'.format(self.n_in_row))
self.current_player = self.players[start_player] # start player
self.availables = list(range(self.width * self.height))
# keep available moves in a list
# once a move has been played, remove it right away
self.states = {}
self.last_move = -1
self.states_sequence = deque(maxlen=self.feature_planes)
self.states_sequence.extendleft([[-1, -1]] * self.feature_planes)
def move_to_location(self, move):
'''
transfer move number to coordinate
3*3 board's moves like:
6 7 8
3 4 5
0 1 2
and move 5's location is (1,2)
'''
h = move // self.width
w = move % self.width
return [h, w]
def location_to_move(self, location):
'''
transfer coordinate to move number
'''
if len(location) != 2:
return -1
h = location[0]
w = location[1]
move = h * self.width + w
if move not in range(self.width * self.height):
return -1
return move
def current_state(self):
'''
return the board state from the perspective of the current player.
state shape: (self.feature_planes+1) x width x height
'''
square_state = np.zeros((self.feature_planes+1, self.width, self.height))
if self.states:
moves, players = np.array(list(zip(*self.states.items())))
# states contain the (key,value) indicate (move,player)
# for example
# self.states.items() get dict_items([(1, 1), (2, 1), (3, 2)])
# zip(*) get [(1, 2, 3), (1, 1, 2)]
# then np.array and get
# moves = np.array([1, 2, 3])
# players = np.array([1, 1, 2])
move_curr = moves[players == self.current_player]
move_oppo = moves[players != self.current_player]
# to construct the binary feature planes as alphazero did
for i in range(self.feature_planes):
# put all moves on planes
if i%2 == 0:
square_state[i][move_oppo // self.width,move_oppo % self.height] = 1.0
else:
square_state[i][move_curr // self.width,move_curr % self.height] = 1.0
# delete some moves to construct the planes with history features
for i in range(0,len(self.states_sequence)-2,2):
for j in range(i+2,len(self.states_sequence),2):
if self.states_sequence[i][1]!= -1:
assert square_state[j][self.states_sequence[i][0] // self.width,self.states_sequence[i][0] % self.height] == 1.0, 'wrong oppo number'
square_state[j][self.states_sequence[i][0] // self.width, self.states_sequence[i][0] % self.height] = 0.
for i in range(1,len(self.states_sequence)-2,2):
for j in range(i+2,len(self.states_sequence),2):
if self.states_sequence[i][1] != -1:
assert square_state[j][self.states_sequence[i][0] // self.width,self.states_sequence[i][0] % self.height] ==1.0, 'wrong player number'
square_state[j][self.states_sequence[i][0] // self.width, self.states_sequence[i][0] % self.height] = 0.
if len(self.states) % 2 == 0:
# if %2==0,it's player1's turn to player,then we assign 1 to the the whole plane,otherwise all 0
square_state[self.feature_planes][:, :] = 1.0 # indicate the colour to play
# we should reverse it before return,for example the board is like
# 0,1,2,
# 3,4,5,
# 6,7,8,
# we will change it like
# 6 7 8
# 3 4 5
# 0 1 2
return square_state[:, ::-1, :]
def do_move(self, move):
'''
update the board
'''
# print(self.states,move,self.current_player,self.players)
self.states[move] = self.current_player
# save the move in states
self.states_sequence.appendleft([move,self.current_player])
# save the last some moves in deque,so as to construct the binary feature planes
self.availables.remove(move)
#remove the played move from self.availables
self.current_player = (
self.players[0] if self.current_player == self.players[1]
else self.players[1]
)
# change the current player
self.last_move = move
def has_a_winner(self):
'''
judge if there's a 5-in-a-row, and which player if so
'''
width = self.width
height = self.height
states = self.states
n = self.n_in_row
moved = list(set(range(width * height)) - set(self.availables))
# moves have been played
if len(moved) < self.n_in_row + 2:
# too few moves to get 5-in-a-row
return False, -1
for m in moved:
h = m // width
w = m % width
player = states[m]
if (w in range(width - n + 1) and
len(set(states.get(i, -1) for i in range(m, m + n))) == 1):
# for each move in moved moves,judge if there's a 5-in-a-row in a line
return True, player
if (h in range(height - n + 1) and
len(set(states.get(i, -1) for i in range(m, m + n * width, width))) == 1):
# for each move in moved moves,judge if there's a 5-in-a-row in a column
return True, player
if (w in range(width - n + 1) and h in range(height - n + 1) and
len(set(states.get(i, -1) for i in range(m, m + n * (width + 1), width + 1))) == 1):
# for each move in moved moves,judge if there's a 5-in-a-row in a top right diagonal
return True, player
if (w in range(n - 1, width) and h in range(height - n + 1) and
len(set(states.get(i, -1) for i in range(m, m + n * (width - 1), width - 1))) == 1):
# for each move in moved moves,judge if there's a 5-in-a-row in a top left diagonal
return True, player
return False, -1
def game_end(self):
'''
Check whether the game is end
'''
end, winner = self.has_a_winner()
if end:
# if one win,return the winner
return True, winner
elif not len(self.availables):
# if the board has been filled and no one win ,then return -1
return True, -1
return False, -1
def get_current_player(self):
'''
return current player
'''
return self.current_player
class Game(object):
'''
game server
'''
def __init__(self, board, **kwargs):
'''
init a board
'''
self.board = board
def graphic(self, board, player1, player2):
'''
Draw the board and show game info
'''
width = board.width
height = board.height
print("Player", player1, "with X".rjust(3))
print("Player", player2, "with O".rjust(3))
print(board.states)
print()
print(' ' * 2, end='')
# rjust()
# http://www.runoob.com/python/att-string-rjust.html
for x in range(width):
print("{0:4}".format(x), end='')
# print('\r\n')
print('\r')
for i in range(height - 1, -1, -1):
print("{0:4d}".format(i), end='')
for j in range(width):
loc = i * width + j
p = board.states.get(loc, -1)
if p == player1:
print('X'.center(4), end='')
elif p == player2:
print('O'.center(4), end='')
else:
print('-'.center(4), end='')
# print('\r\n') # new line
print('\r')
def start_play(self, player1, player2, start_player=0, is_shown=1,print_prob =True):
'''
start a game between two players
'''
if start_player not in (0, 1):
raise Exception('start_player should be either 0 (player1 first) '
'or 1 (player2 first)')
self.board.init_board(start_player)
p1, p2 = self.board.players
# print(p1,p2)
player1.set_player_ind(p1)
player2.set_player_ind(p2)
players = {p1: player1, p2: player2}
if is_shown:
self.graphic(self.board, player1.player, player2.player)
while True:
current_player = self.board.get_current_player()
player_in_turn = players[current_player]
move,move_probs = player_in_turn.get_action(self.board,is_selfplay=False,print_probs_value=print_prob)
self.board.do_move(move)
if is_shown:
print('player %r move : %r' % (current_player, [move // self.board.width, move % self.board.width]))
self.graphic(self.board, player1.player, player2.player)
end, winner = self.board.game_end()
if end:
if is_shown:
if winner != -1:
print("Game end. Winner is", players[winner])
else:
print("Game end. Tie")
return winner
def start_play_with_UI(self, AI, start_player=0):
'''
a GUI for playing
'''
AI.reset_player()
self.board.init_board()
current_player = SP = start_player
UI = GUI(self.board.width)
end = False
while True:
print('current_player', current_player)
if current_player == 0:
UI.show_messages('Your turn')
else:
UI.show_messages('AI\'s turn')
if current_player == 1 and not end:
move, move_probs = AI.get_action(self.board, is_selfplay=False, print_probs_value=1)
else:
inp = UI.get_input()
if inp[0] == 'move' and not end:
if type(inp[1]) != int:
move = UI.loc_2_move(inp[1])
else:
move = inp[1]
elif inp[0] == 'RestartGame':
end = False
current_player = SP
self.board.init_board()
UI.restart_game()
AI.reset_player()
continue
elif inp[0] == 'ResetScore':
UI.reset_score()
continue
elif inp[0] == 'quit':
exit()
continue
elif inp[0] == 'SwitchPlayer':
end = False
self.board.init_board()
UI.restart_game(False)
UI.reset_score()
AI.reset_player()
SP = (SP+1) % 2
current_player = SP
continue
else:
# print('ignored inp:', inp)
continue
# print('player %r move : %r'%(current_player,[move//self.board.width,move%self.board.width]))
if not end:
# print(move, type(move), current_player)
UI.render_step(move, self.board.current_player)
self.board.do_move(move)
# print('move', move)
# print(2, self.board.get_current_player())
current_player = (current_player + 1) % 2
# UI.render_step(move, current_player)
end, winner = self.board.game_end()
if end:
if winner != -1:
print("Game end. Winner is player", winner)
UI.add_score(winner)
else:
print("Game end. Tie")
print(UI.score)
print()
def start_self_play(self, player, is_shown=0):
'''
start a self-play game using a MCTS player, reuse the search tree,
and store the self-play data: (state, mcts_probs, z) for training
'''
self.board.init_board()
p1, p2 = self.board.players
states, mcts_probs, current_players = [], [], []
while True:
move, move_probs = player.get_action(self.board,
is_selfplay=True,
print_probs_value=False)
# store the data
states.append(self.board.current_state())
mcts_probs.append(move_probs)
current_players.append(self.board.current_player)
# perform a move
self.board.do_move(move)
if is_shown:
self.graphic(self.board, p1, p2)
end, winner = self.board.game_end()
if end:
# winner from the perspective of the current player of each state
winners_z = np.zeros(len(current_players))
if winner != -1:
winners_z[np.array(current_players) == winner] = 1.0
winners_z[np.array(current_players) != winner] = -1.0
# reset MCTS root node
player.reset_player()
if is_shown:
if winner != -1:
print("Game end. Winner is player:", winner)
else:
print("Game end. Tie")
return winner, zip(states, mcts_probs, winners_z)