def erase_column(self, player):
'''
Parameters
----------
player : PLAYER
player with a column deletion in his board
Returns
-------
None.
Removes a column if 3 card values in the same column (not hidden) are equal
'''
flag = False
for i in range(4):
if player.board[0][i].value == player.board[1][
i].value == player.board[2][i].value:
if player.board[0][i].hidden == player.board[1][
i].hidden == player.board[2][i].hidden and not (
player.board[2][i].hidden):
if player.board[0][i].value != 0:
card1 = Card(player.board[0][i].value)
card2 = Card(player.board[0][i].value)
card3 = Card(player.board[0][i].value)
self.defausse.append(card1)
self.defausse.append(card2)
self.defausse.append(card3)
player.board[0][i].value, player.board[1][
i].value, player.board[2][i].value = 0, 0, 0
flag = True
return flag
def create_initial_deck_and_market():
# Create the whole deck
cards = [Card(c) for c in config.get('cards')]
deck = Game.create_initial_deck(cards)
# Initial market will be first 9
initial_market = Market(deck[:9])
rest = deck[9:]
# Add the phase 3 card
rest.append(Card(config.get('stage_three_card')))
return initial_market, rest
def setBoard(self):
b = [] # Card[]
t = []
for i in range(40):
t.append(-1)
# Add PropertyCards
# for i in range(len(ApplicationContext().get_instance().getCards())):
# b[ApplicationContext().get_instance().getCards()[i].getPosition()] = ApplicationContext().get_instance().getCards()[i]
# t[ApplicationContext().get_instance().getCards()[i].getPosition()] = 0
# Add CommunityChestCards
# for i in range(len(ApplicationContext().get_instance().getCommunityCardPositions())):
#b[ApplicationContext().get_instance().getCommunityCardPositions()[i]] = CommandCard()
#t[ApplicationContext().get_instance().getCommunityCardPositions()[i]] = 1
# Add ChanceCards
# for i in range(len(ApplicationContext().get_instance().getChanceCardPositions())):
# b[ApplicationContext().get_instance().getChanceCardPositions()[i]] = CommandCard()
# t[ApplicationContext().get_instance().getChanceCardPositions()[i]] = 2
b = [Card() for i in range(40)] # empty card array
t = [-1] * 40 # int array of -1
# Specify that every position left on board is a special position ( GO, Jail, etc... )
# We'll take care of what occurs on every case in a different method
for i in range(len(b)):
if t[i] < 0:
t[i] = 3
b[i] = SpecialPositionCard()
def initialiseBoard(self):
b = [Card() for i in range(40)] # empty card array
t = [-1] * 40 # int array of -1
# Add PropertyCards
for i in range(len(self.getCards())):
b[int(self.getCards()[i].getPosition())] = self.getCards()[i]
t[int(self.getCards()[i].getPosition())] = 0
# Add CommunityChestCards
for i in range(len(self.getCommunityCardPositions())):
b[int(self.getCommunityCardPositions()[i])] = CommandCard()
t[int(self.getCommunityCardPositions()[i])] = 1
# Add ChanceCards
for i in range(len(self.getChanceCardPositions())):
b[int(self.getChanceCardPositions()[i])] = CommandCard()
t[int(self.getChanceCardPositions()[i])] = 2
# Specify that every position left on board is a special position ( GO, Jail, etc... )
# We'll take care of what occurs on every case in a different method
for i in range(len(b)):
if t[i] < 0:
t[i] = 3
b[i] = SpecialPositionCard()
# Set the global board parameter
self.setBoard(Board(b, t))
def reset(self):
'''
Returns
-------
List
Observation
Reset the Skyjo Environment and return an initial observation
Basically doing the same thing as __init__
'''
self.defausse = []
self.history = []
self.cards_thrown = []
self.state = 0
self.reward = 0
self.not_done = False
self.unfinished = True
de = [-2] * 5 + [-1] * 10 + [0] * 15 + [
i for i in range(1, 13) for j in range(10)
]
self.deck = []
self.deck_card = Card(5)
self.columns_made = []
self.reward2 = 0
for u in de:
self.deck.append(Card(u))
self.setup()
if self.num_players == 1:
L = [-2, 0, -2] + [-2] * 12
H = [12, 100, 12] + [12] * 12
self.observation_space = spaces.Box(low=np.array(L),
high=np.array(H))
board_int, board_bool = self.players[0].get_board_as_int(
self.mean_value_deck())
self.observation = np.concatenate((np.array(
[self.defausse[-1].value, self.state,
self.deck_card.value]), board_int))
#self.observation = np.concatenate((self.observation,board_bool))
elif self.num_players == 2:
#L = [-2,0,-2]+[-2]*12+[0]*12+[-2]*12+[0]*12
#H = [12,100,12]+[12]*12+[1]*12+[12]*12+[1]*12
#L = [-2,0,-2]+[-2]*12+[-20,0] # only score of the opponent
#H = [12,100,12]+[12]*12+[150,10]
L = [
-2, 0, -2
] + [-2] * 12 + [-2] * 12 # board of the opponent without booleans
H = [12, 100, 12] + [12] * 12 + [12] * 12
self.observation_space = spaces.Box(low=np.array(L),
high=np.array(H))
board_int, board_bool = self.players[0].get_board_as_int(
self.mean_value_deck())
self.observation = np.concatenate((np.array(
[self.defausse[-1].value, self.state,
self.deck_card.value]), board_int))
#self.observation = np.concatenate((self.observation,board_bool))
board_int, board_bool = self.players[1].get_board_as_int(
self.mean_value_deck())
self.observation = np.concatenate((self.observation, board_int))
#self.observation = np.concatenate((self.observation,board_bool))
#score = self.players[1].compute_score(self.mean_value_deck())
#tiles = len(self.players[1].undiscovered_tiles())
#self.observation = np.concatenate((self.observation,np.array([score,tiles])))
return self.observation
def __init__(self, players, human_mode=False):
'''
Parameters
----------
players : List
List containing player instances
Returns
-------
None.
Initializes the Skyjo environment
'''
self.players = players # List of players in the game
# not_done is a Boolean, as a player's turn can be composed of 2 actions,
# It is True if the player has not done all his action, False otherwise
self.not_done = False
self.drew = False
self.num_players = len(players) # The number of players
self.action_space = spaces.MultiDiscrete([2, 13])
self.take = 0 # Int representing a global of an action
self.draw = 1 # Int representing a global of an action
self.throw = 2 # Int representing a global of an action
self.defausse = [] # The discard pile, list containing discarded cards
self.deck_card = Card(5) # First deck_card
self.reward = 0 # The reward
self.state = 0
self.unfinished = True
self.cards_thrown = []
self.human_mode = human_mode
self.columns_made = []
self.reward2 = 0
# Deck card initialized as the real game
de = [-2] * 5 + [-1] * 10 + [0] * 15 + [
i for i in range(1, 13) for j in range(10)
]
self.deck = [] # The deck, list of cards composing the deck
for u in de:
self.deck.append(Card(u))
self.deck_copy = self.deck.copy()
self.setup() # Call set up, initialize the env
L = [-2, 0, -2] + [-2] * 12
H = [12, 100, 12] + [12] * 12
self.observation_space = spaces.Box(low=np.array(L), high=np.array(H))
# The observation space, created respecting GYM env
if self.num_players == 1:
L = [-2, 0, -2] + [-2] * 12
H = [12, 100, 12] + [12] * 12
self.observation_space = spaces.Box(low=np.array(L),
high=np.array(H))
board_int, board_bool = self.players[0].get_board_as_int(
self.mean_value_deck())
self.observation = np.concatenate((np.array(
[self.defausse[-1].value, self.state,
self.deck_card.value]), board_int))
#self.observation = np.concatenate((self.observation,board_bool))
elif self.num_players == 2:
#L = [-2,0,-2]+[-2]*12+[0]*12+[-2]*12+[0]*12 # boolean boards and board of the opponent
#H = [12,100,12]+[12]*12+[1]*12+[12]*12+[1]*12
#L = [-2,0,-2]+[-2]*12+[-20,0] # only score of the opponent
#H = [12,100,12]+[12]*12+[150,10]
L = [
-2, 0, -2
] + [-2] * 12 + [-2] * 12 # board of the opponent without booleans
H = [12, 100, 12] + [12] * 12 + [12] * 12
self.observation_space = spaces.Box(low=np.array(L),
high=np.array(H))
board_int, board_bool = self.players[0].get_board_as_int(
self.mean_value_deck())
self.observation = np.concatenate((np.array(
[self.defausse[-1].value, self.state,
self.deck_card.value]), board_int))
#self.observation = np.concatenate((self.observation,board_bool))
board_int, board_bool = self.players[1].get_board_as_int(
self.mean_value_deck())
self.observation = np.concatenate((self.observation, board_int))
def __init__(self,players,human_mode = False):
'''
Parameters
----------
players : List
List containing player instances
Returns
-------
None.
Initializes the Skyjo environment
'''
self.players = players # List of players in the game
self.not_done = False # Boolean, as a player's turn can be composed of 2 actions, True if the player has not done all his actions
self.drew = False
self.num_players = len(players) # The number of players
self.action_space = spaces.MultiDiscrete([2,13])
self.take = 0 # Int representing a global of an action
self.draw = 1 # Int representing a global of an action
self.throw = 2 # Int representing a global of an action
self.defausse = [] # The discard pile, list containing discarded cards
self.deck_card = Card(5) # First deck_card
self.reward = 0 # The reward
self.state = 0
self.cards_thrown = []
self.testing = False
self.cards_known = []
self.unfinished = True
self.human_mode = human_mode
self.columns_made = []
self.reward2 = 0
# Deck card initialized as the real game
de = [-2]*5 + [-1]*10 + [0]*15 + [i for i in range(1,13) for j in range(10)]
self.deck=[] # The deck, list of cards composing the deck
for u in de:
self.deck.append(Card(u))
self.deck_copy = self.deck.copy()
self.setup() # Call set up, initialize the env
# The observation space, created respecting GYM env
if self.num_players == 1:
L = [-2,0,-2]+[-2]*12
H = [12,100,12]+[12]*12
self.observation_space = spaces.Box(low=np.array(L),high=np.array(H))
board_int,board_bool = self.players[0].get_board_as_int(self.mean_value_deck())
self.observation = np.concatenate((np.array([self.defausse[-1].value,self.state,self.deck_card.value]),board_int))
#self.observation = np.concatenate((self.observation,board_bool))
elif self.num_players == 2:
#L = [-2,0,-2]+[-2]*12+[0]*12+[-2]*12+[0]*12
#H = [12,100,12]+[12]*12+[1]*12+[12]*12+[1]*12
L = [-2,0,-2]+[-2]*12+[-2]*12
H = [12,100,12]+[12]*12+[12]*12
self.observation_space = spaces.Box(low=np.array(L),high=np.array(H))
board_int,board_bool = self.players[0].get_board_as_int(self.mean_value_deck())
self.observation = np.concatenate((np.array([self.defausse[-1].value,self.state,self.deck_card.value]),board_int))
#self.observation = np.concatenate((self.observation,board_bool))
board_int,board_bool = self.players[1].get_board_as_int(self.mean_value_deck())
self.observation = np.concatenate((self.observation,board_int))
#self.observation = np.concatenate((self.observation,board_bool))
#score = self.players[1].compute_score(self.mean_value_deck())
#tiles = len(self.players[1].undiscovered_tiles())
#self.observation = np.concatenate((self.observation,np.array([score,tiles])))
# Display variables from tkinter library
if self.testing:
self.root = Tk()
self.canvas = Canvas(self.root, bg="white", height=650, width=1000)