def __init__(self):
self.deck = treys.Deck()
self.evaluator = treys.Evaluator()
self.reward_range = (
-1, 1) # we will process the reward to fit in [-1,1] from [-10,10]
self.metadata = {'render_modes': ['ansi']}
self.observation_space = OFCSObservationSpace(356)
self.action_space = gym.spaces.Discrete(2)
self.done = False
self.obs = self.reset()
def __init__(self):
"""
Initialize a new hand.
"""
self.board_cards = []
self.hole_cards_as_treys = []
self.i_am_big_blind = False
self.pot_size = 0
self.current_stage = ''
self.evaluator = treys.Evaluator()
def build_network_input(self, player_idx, round, current_bets, min_raise, prev_round_investment, folded, last_raiser,
hole_cards, community_cards):
# First convert the treys card IDs into indices
hole_cards_converted = 13 * np.log2(np.right_shift(hole_cards, 12) & 0xF) + (np.right_shift(hole_cards, 8) & 0xF)
community_cards_converted = 13 * np.log2(np.right_shift(community_cards, 12) & 0xF) + (
np.right_shift(community_cards, 8) & 0xF)
# Then convert those indices into 1h
hole_cards_1h = (np.arange(52) == hole_cards_converted[..., None] - 1).astype(int)
known_community_cards_1h = (np.arange(53) == community_cards_converted[..., None] - 1).astype(int)
# Fill missing community cards with zero
missing_community_cards = np.zeros((self.BATCH_SIZE, 5 - community_cards.shape[1], 53))
# Have a 53rd column in the 1h to indicate missing cards, and fill that with ones where relevant
missing_community_cards[:, :, -1] = 1
community_cards_1h = np.concatenate((known_community_cards_1h, missing_community_cards), axis=1)
player_data = np.zeros((self.BATCH_SIZE, 5, self.N_PLAYERS))
# Who folded already
player_data[:, 0, :] = folded
# Who put how much total into the pot
player_data[:, 1, :] = (prev_round_investment + current_bets) / self.INITAL_CAPITAL
# Who put how much this round
player_data[:, 2, :] = (current_bets) / self.INITAL_CAPITAL
# Who was the last to raise
player_data[:, 3, :] = np.eye(self.N_PLAYERS)[last_raiser]
# Reorder the first four to correspond to player_idx
player_data = np.concatenate((player_data[:, :, player_idx:], player_data[:, :, :player_idx]), axis=2)
# Which player are we
player_data[:, 4, player_idx] = 1
rank_bin = np.zeros(self.BATCH_SIZE)
evaluator = treys.Evaluator()
for i in range(self.BATCH_SIZE):
if round != constants.PRE_FLOP:
rank = evaluator.evaluate(community_cards[i].tolist(), hole_cards[i].tolist())
percentile = 1 - self.ranktable[round - constants.FLOP, rank]
rank_bin[i] = percentile
else:
sorted_hole = sorted(hole_cards[i].tolist())
percentile = self.preflop_table.index(sorted_hole) / len(self.preflop_table)
rank_bin[i] = percentile
tail_data = np.zeros((self.BATCH_SIZE, 5))
tail_data[:, round] = 1
assert(round < 4)
tail_data[:, 4] = rank_bin
network_input = np.concatenate((hole_cards_1h.reshape(self.BATCH_SIZE, -1),
community_cards_1h.reshape(self.BATCH_SIZE, -1),
player_data.reshape(self.BATCH_SIZE, -1), tail_data.reshape(self.BATCH_SIZE, -1)),
axis=1)
assert (network_input.shape[1] == self.obs_dim)
return network_input
def __init__(self, num_players, starting_big_blind, hands_per_bb):
self.players = []
self.order = [i for i in range(num_players)]
self.board = []
self.active = [True for _ in range(num_players)]
self.deck = utils.make_deck()
self.evaluator = treys.Evaluator()
self.big_blind = starting_big_blind
self.pot = 0
self.current_bet = 0
self.hands_per_bb = hands_per_bb
self.round_counter = 0
self.aggressor = None
def evaluation(my_cards, known_cards):
hand = []
for new_card in my_cards:
hand.append(treys.Card.new(new_card))
board = []
for new_card in known_cards:
board.append(treys.Card.new(new_card))
evaluator = treys.Evaluator()
if len(hand) != 0:
# print(board, hand)
return evaluator.evaluate(board, hand)
else:
return 10000
def __init__(self):
self.action_space = spaces.Discrete(26) # 5 choose 3, plus 5 choose 2, plus 5, plus 1
self.observation_space = spaces.Discrete(2598960) # number of five card hand combos
self.deck = treys.Deck()
self.hand = self.deal_hand()
# self.done = False
self.evaluator = treys.Evaluator()
# SET THE REWARDS
self.RANK_CLASS_STRING_TO_REWARD = {
'Straight Flush': 100,
'Four of a Kind': 25,
'Full House': 9,
'Flush': 6,
'Straight': 4,
'Three of a Kind': 3,
'Two Pair': 2,
'Pair': 1,
'High Card': -1}
self.RANK_CLASS_TO_STRING = {
1: "Straight Flush",
2: "Four of a Kind",
3: "Full House",
4: "Flush",
5: "Straight",
6: "Three of a Kind",
7: "Two Pair",
8: "Pair",
9: "High Card"
}
# get the hand rank for jacks
# so we can later check if the player hand is better than jacks
self._jacks_hand_score = self.evaluator.evaluate( [
treys.Card.new('Jh'),
treys.Card.new('Js'),
treys.Card.new('2s')
],
[
treys.Card.new('3h'),
treys.Card.new('4d')
]
)
def getExpectedHandStrengthOdds(
p1_hand, # The hole cards
numIterations, # The number of iterations to run the simulation for
numPlayers, # The number of players in the hand
numBoardCardsToCome, # The number of board cards still to come
existingBoardCards # The existing board cards
):
debug = False
# pretend you are playing with n random players
# for numIterations hands with numBoardCardsToCome and existingBoardCards board cards
evaluator = treys.Evaluator()
winner = [0] * numPlayers
for j in range(0, numIterations):
# create the partial deck that excludes your cards and the board cards
p1_HandAndExistingBoardCards = p1_hand + existingBoardCards
pDeck = partialDeck.PartialDeck(p1_HandAndExistingBoardCards)
# create the hands.
# By convention your hand is in index=0
hands = []
hands.append(p1_hand)
for i in range(1, numPlayers):
hands.append(pDeck.draw(2))
# Now, draw the board cards
boardCardsToCome = pDeck.draw(numBoardCardsToCome)
if (debug):
print("Existing board cards=")
treys.Card.print_pretty_cards(existingBoardCards)
print("New board cards=")
treys.Card.print_pretty_cards(boardCardsToCome)
fullBoardCards = existingBoardCards + boardCardsToCome
numTies = gameUtilities.findWinners(debug, evaluator, numPlayers,
hands, fullBoardCards, winner)
for i in range(0, numPlayers):
winner[i] /= numIterations
return winner
def getpreFlopOdds(
p1_hand, # The hole cards
numIterations, # The number of iterations to run the simulation for
numPlayers, # The number of players in the hand
numBoardCards # The number of board cards
):
debug = False
# pretend you are playing with n random players
# for numIterations hands with numBoardCards
evaluator = treys.Evaluator()
winner = [0] * numPlayers
for j in range(0, numIterations):
# create the partial deck that excludes your cards
pDeck = partialDeck.PartialDeck(p1_hand)
# create the hands.
# By convention your hand is in index=0
hands = []
hands.append(p1_hand)
for i in range(1, numPlayers):
hands.append(pDeck.draw(2))
# Now, draw the board cards
boardCards = pDeck.draw(numBoardCards)
if (debug):
print("Board cards=")
treys.Card.print_pretty_cards(boardCards)
numTies = gameUtilities.findWinners(debug, evaluator, numPlayers,
hands, boardCards, winner)
for i in range(0, numPlayers):
winner[i] /= numIterations
return winner
def evaluate_table(n_cards):
stats = np.zeros(N_SCORES)
deck = treys.Deck()
evaluator = treys.Evaluator()
for k in range(N_LOOPS):
for l in range(PRINT_FREQUENCY):
deck.shuffle()
hand = deck.draw(n_cards)
score = evaluator.evaluate([], hand)
stats[score] += 1
summation = np.cumsum(stats)
summation /= summation[-1]
print("-" * 80)
print("Table {0} of {1} cards known:".format(k, n_cards))
for i in range(1, N_BINS):
idx = int(i * N_SCORES / N_BINS)
print("\tHand value {0} loses to {1}% of possible hands".format(
idx, int(100 * summation[idx])))
sns.lineplot(x=np.arange(N_SCORES), y=stats / np.sum(stats))
plt.savefig("dist_{}.png".format(n_cards))
plt.clf()
np.save("dist_{}.npy".format(n_cards), summation)
def compute_strat_vector(agent, verbose=False):
# Mutable
N_CARD_PERCENTILE_BINS = 10
N_OWN_POT_SIZE_BINS = 6
N_MIN_SAMPLES_PER_BIN = 25
BATCH_SIZE = 10000
INITIAL_CAPITAL = 200
MIN_RAISE = 4
ACTION_SPACE_BINS = INITIAL_CAPITAL // 4 + 3
# Fixed
N_ROUNDS = 4
N_SEATING_POSITIONS = 6
N_MAX_ACTIVE_PLAYERS = 5
N_PLAYERS_TOTAL = 6
path_root = "strategy"
n_games = np.zeros((
N_SEATING_POSITIONS,
N_ROUNDS,
N_MAX_ACTIVE_PLAYERS - 2,
N_OWN_POT_SIZE_BINS,
N_CARD_PERCENTILE_BINS
))
strategy = np.zeros((
N_SEATING_POSITIONS,
N_ROUNDS,
N_MAX_ACTIVE_PLAYERS - 2,
N_OWN_POT_SIZE_BINS,
N_CARD_PERCENTILE_BINS,
ACTION_SPACE_BINS
))
agent.initialize(BATCH_SIZE, INITIAL_CAPITAL, N_PLAYERS_TOTAL)
ranktable = np.stack((
np.load(os.path.join(path_root, "5card_rank_percentile.npy")),
np.load(os.path.join(path_root, "6card_rank_percentile.npy")),
np.load(os.path.join(path_root, "7card_rank_percentile.npy"))
))
preflop_table = np.load(os.path.join(path_root, "preflop_ranks.npy")).tolist()
evaluator = treys.Evaluator()
lowest_bin_count = np.amin(n_games)
while lowest_bin_count <= N_MIN_SAMPLES_PER_BIN:
if verbose:
print("Starting filling round")
print("\tLowest number currently:", lowest_bin_count)
print("\tNumber of bins at lowest:", np.count_nonzero(n_games == lowest_bin_count))
print("\tAverage bin count:", np.mean(n_games))
for round in range(0, N_ROUNDS):
n_community_cards = [0, 3, 4, 5][round]
for seating_position in range(N_SEATING_POSITIONS):
for n_active_players in range(2, N_MAX_ACTIVE_PLAYERS):
for own_pot_idx in range(N_OWN_POT_SIZE_BINS):
own_pot = int(INITIAL_CAPITAL * own_pot_idx / N_OWN_POT_SIZE_BINS)
community_cards, all_hole_cards = generate_cards(N_PLAYERS_TOTAL, BATCH_SIZE)
hole_cards = all_hole_cards[:, seating_position, :]
rank_bin = np.zeros(BATCH_SIZE)
folded_players = np.zeros((BATCH_SIZE, N_PLAYERS_TOTAL - n_active_players), dtype=int)
active_players = np.zeros((BATCH_SIZE, n_active_players), dtype=int)
last_raiser = np.zeros(BATCH_SIZE, dtype=int)
for i in range(BATCH_SIZE):
folded_row = random.sample([x for x in range(N_PLAYERS_TOTAL) if x != seating_position], N_PLAYERS_TOTAL - n_active_players)
active_row = [x for x in range(N_PLAYERS_TOTAL) if x not in folded_row]
folded_players[i, :] = folded_row
active_players[i, :] = active_row
last_raiser[i] = random.choice(active_row)
if round != constants.PRE_FLOP:
rank = evaluator.evaluate(community_cards[i, :n_community_cards].tolist(), hole_cards[i].tolist())
percentile = 1 - ranktable[round - constants.FLOP, rank]
rank_bin[i] = int(percentile * N_CARD_PERCENTILE_BINS)
else:
sorted_hole = sorted(hole_cards[i].tolist())
percentile = preflop_table.index(sorted_hole) / len(preflop_table)
rank_bin[i] = int(percentile * N_CARD_PERCENTILE_BINS)
current_bets = np.zeros((BATCH_SIZE, N_PLAYERS_TOTAL))
prev_round_investment = np.zeros((BATCH_SIZE, N_PLAYERS_TOTAL))
if round != constants.PRE_FLOP:
portions = np.random.random(size=(BATCH_SIZE, round + 1))
# If we are the last raiser, then we raised in the last round, which means there are no bets standing this round
portions[last_raiser == seating_position, -1] = 0
portions /= np.sum(portions, axis=1)[:, None]
self_prev_investment = (np.sum(portions[:, :-1], axis=1) * own_pot)[:, None]
self_current_investment = (portions[:, -1] * own_pot)[:, None]
prev_round_investment[
np.arange(prev_round_investment.shape[0])[:, None], folded_players
] += np.random.random(size=folded_players.shape) * self_prev_investment
# https://stackoverflow.com/questions/20103779/index-2d-numpy-array-by-a-2d-array-of-indices-without-loops
prev_round_investment[np.arange(prev_round_investment.shape[0])[:, None], active_players] += self_prev_investment
current_bets[np.arange(current_bets.shape[0])[:, None], active_players] += self_current_investment
# small blind + big blind
prev_round_investment[:, 0] = np.maximum(prev_round_investment[:, 0], 2)
prev_round_investment[:, 1] = np.maximum(prev_round_investment[:, 1], 4)
else:
# https://stackoverflow.com/questions/20103779/index-2d-numpy-array-by-a-2d-array-of-indices-without-loops
current_bets[np.arange(current_bets.shape[0])[:, None], folded_players] += np.random.random(size=folded_players.shape) * own_pot
current_bets[np.arange(current_bets.shape[0])[:, None], active_players] += own_pot
_, last_raiser_col = np.where(active_players == last_raiser[:, None])
_, seating_position_col = np.where(active_players == seating_position)
for i in range(BATCH_SIZE):
if last_raiser_col[i] < seating_position_col[i]:
for active_player in active_players[i, last_raiser_col[i]:seating_position_col[i]]:
current_bets[i, active_player] += MIN_RAISE
elif last_raiser_col[i] > seating_position_col[i]:
for active_player in active_players[i, last_raiser_col[i]:]:
current_bets[i, active_player] += MIN_RAISE
for active_player in active_players[i, :seating_position_col[i]]:
current_bets[i, active_player] += MIN_RAISE
folded_1h = np.zeros((BATCH_SIZE, N_PLAYERS_TOTAL))
folded_1h[folded_players] = 1
no_bet_rounds = np.where(~current_bets.any(axis=1))[0]
last_raiser[no_bet_rounds] = np.argmax(prev_round_investment[no_bet_rounds, :], axis=1)
actions, amounts = agent.act(
player_idx=seating_position,
round=round,
active_games=np.ones(BATCH_SIZE),
current_bets=current_bets,
min_raise=np.full(BATCH_SIZE, MIN_RAISE),
prev_round_investment=prev_round_investment,
folded=folded_1h,
last_raiser=last_raiser,
hole_cards=hole_cards,
community_cards=community_cards[:, :n_community_cards]
)
# print("Seating position", seating_position)
# print("Round", round)
# print("Active players", active_players[5])
# print("Current bets", current_bets[5, :])
# print("Prev round investment", prev_round_investment[5, :])
# print("Last raiser", last_raiser[5])
# print("Card rank", rank_bin[5])
# print("Action", actions[5])
# print("Amount", amounts[5])
# input()
action_bins = 3 + np.floor(amounts * (ACTION_SPACE_BINS - 3) / (INITIAL_CAPITAL+1)).astype(int)
action_bins[actions == constants.FOLD] = constants.FOLD
action_bins[actions == constants.CALL] = constants.CALL
action_bins[np.logical_and(actions == constants.CALL, current_bets.sum(axis=1) == 0)] = constants.CHECK
for card_rank in range(N_CARD_PERCENTILE_BINS):
n_games[seating_position, round, n_active_players - 2, own_pot_idx, card_rank] += np.count_nonzero(
rank_bin == card_rank
)
for action in range(ACTION_SPACE_BINS):
strategy[
seating_position, round, n_active_players - 2, own_pot_idx, card_rank, action
] += np.count_nonzero(action_bins[rank_bin == card_rank] == action)
lowest_bin_count = np.amin(n_games)
if verbose:
print("Strategy vector finished, min {0}, max {1} games.".format(np.amin(n_games), np.amax(n_games)))
return strategy, n_games
async def on_message(message):
if message.author == client.user:
return
if message.content.startswith('!poker'):
user = bot.user
user2 = bot.user
author: discord.Member = message.author
channel = message.channel
deck = treys.Deck()
evaluator = treys.Evaluator()
board = deck.draw(5)
p1_hand = deck.draw(0)
await channel.send(treys.Card.print_pretty_cards(board + p1_hand))
change = await channel.send(
str(author.mention) +
', quantas cartas você quer trocar? (Espere todos os emotes aparecerem, você tem 20 segundos)'
)
await change.add_reaction('0️⃣')
await change.add_reaction('1️⃣')
await change.add_reaction('2️⃣')
await change.add_reaction('3️⃣')
await change.add_reaction('4️⃣')
await change.add_reaction('5️⃣')
def check(reaction, user):
if str(reaction.emoji) == '0️⃣':
return user == message.author and str(reaction.emoji) == '0️⃣'
if str(reaction.emoji) == '1️⃣':
return user == message.author and str(reaction.emoji) == '1️⃣'
elif str(reaction.emoji) == '2️⃣':
return user == message.author and str(reaction.emoji) == '2️⃣'
elif str(reaction.emoji) == '3️⃣':
return user == message.author and str(reaction.emoji) == '3️⃣'
elif str(reaction.emoji) == '4️⃣':
return user == message.author and str(reaction.emoji) == '4️⃣'
elif str(reaction.emoji) == '5️⃣':
return user == message.author and str(reaction.emoji) == '5️⃣'
def check2(reaction):
if str(reaction.emoji) == '0️⃣':
return -1
if str(reaction.emoji) == '1️⃣':
return 0
elif str(reaction.emoji) == '2️⃣':
return 1
elif str(reaction.emoji) == '3️⃣':
return 2
elif str(reaction.emoji) == '4️⃣':
return 3
elif str(reaction.emoji) == '5️⃣':
return 4
try:
while (user != message.author):
reaction, user = await bot.wait_for('reaction_add',
timeout=20.0,
check=check)
a = check2(reaction) + 1
x = np.zeros(a)
if (a != 0):
change = await channel.send(
str(author.mention) +
', quais cartas você quer trocar? (Espere todos os emotes aparecerem, você tem 20 segundos)'
)
await change.add_reaction('1️⃣')
await change.add_reaction('2️⃣')
await change.add_reaction('3️⃣')
await change.add_reaction('4️⃣')
await change.add_reaction('5️⃣')
for i in range(a):
while (user2 != message.author):
reaction, user2 = await bot.wait_for('reaction_add',
timeout=20.0,
check=check)
user2 = bot.user
x[i] = check2(reaction)
x.sort()
x = x[::-1]
for i in range(a):
board[int(x[i])] = deck.draw(1)
except asyncio.TimeoutError:
await channel.send(str(author.mention) + ', demorou demais')
else:
await channel.send(treys.Card.print_pretty_cards(board + p1_hand))
p1_score = evaluator.evaluate(board, p1_hand)
p1_class = evaluator.get_rank_class(p1_score)
await channel.send(
str(author.mention) + ', você tirou um ' +
evaluator.class_to_string(p1_class))
await bot.process_commands(message)
def __init__(self):
self.evaluator = treys.Evaluator()
'startingStack=', gameDefinition.startingStack[i])
# Start at handNumber = 0
players.insert(i, gameUtilities.Player(i, gameDefinition, 0))
print('players[',i,'].stackSize=', players[i].stackSize)
# Once you've got that sorted out then move on to the end of hand
# play and how you store the values of the current hand (cards, bet, stack)
# in the historical list of values.
print ('players[0].stackSize=', players[0].stackSize)
# Create a list to hold the player winnings (needed for the ACPC because the player's
# stacks are reset after every hand)
playerCumulativeWinnings = [0] * gameDefinition.numPlayers
# Create the hand evaluator that will tell you who won a hand
handEvaluator = treys.Evaluator()
# start communicating with the dealer
if (socketComms):
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((HOST, PORT))
versionString = "VERSION:2.0.0\r\n"
s.send(versionString.encode('utf-8'))
previousHandNumber = -1
cont= True
dataIn = ''
handString = ''
while (cont == True):
