def __init__(self):
params = {}
params['root_node'] = {}
params['root_node']['board'] = card_to_string.string_to_board('')
params['root_node']['street'] = 0
params['root_node']['current_player'] = constants.players.P1
params['root_node']['bets'] = arguments.Tensor([100, 100])
params['limit_to_street'] = False
builder = PokerTreeBuilder()
self.root_node = builder.build_tree(params)
# print(self.builder.node_id_acc)
filling.fill_uniform(self.root_node)
self.state = GameState()
self._cached_terminal_equities = {}
def compute_action(self, state):
# convert tensor for rl
builder = PokerTreeBuilder()
state_tensor = builder.statenode_to_tensor(state)
# !!!! the return action is a longTensor[[]]
# action_id = (self.table_sl.select_action(state) if random.random() > arguments.eta \
# else self.dqn_optim.select_action(state_tensor))[0][0]
# action_id = self.table_sl.select_action(state)[0][0]
action_id = self.net_sl[state.node.current_player].select_action(
state_tensor).item()
# print('_____________')
# print(action_id)
# print('_____________')
# action['action: ,'raise_amount': ]
action = {}
#fold
if action_id == 0:
action['action'] = constants.acpc_actions.fold
# call
elif action_id == 1 or action_id >= game_settings.actions_count:
action['action'] = constants.acpc_actions.ccall
#raise
elif action_id > 1:
# get possible to determine the raising size
bet_sizding = BetSizing(arguments.Tensor(arguments.bet_sizing))
possible_bets = bet_sizding.get_possible_bets(state.node)
if possible_bets.dim() != 0:
possible_bet = possible_bets[:, state.node.current_player]
else:
action['action'] = constants.acpc_actions.ccall
return action
raise_action_id = action_id - 2 # to override fold and call action
# node possible bet in this state so call
action['action'] = constants.acpc_actions.rraise
if (len(possible_bet) <= raise_action_id):
action['raise_amount'] = possible_bet[len(possible_bet) -
1].item()
else:
action['raise_amount'] = possible_bet[raise_action_id].item(
) # to override fold and call action
else:
assert (False) #invaild actions
return action
def test(self, table_sl):
builder = PokerTreeBuilder()
params = {}
params['root_node'] = {}
params['root_node']['board'] = card_to_string.string_to_board('')
params['root_node']['street'] = 0
params['root_node']['current_player'] = constants.players.P1
params['root_node']['bets'] = arguments.Tensor([100, 100])
params['limit_to_street'] = False
tree = builder.build_tree(params)
# table_sl = torch.load('/home/mjb/Nutstore/deepStack/Data/Model/Iter:' + str(model_num) + '.sl')
#constract the starting range
filling = StrategyFilling()
range1 = card_tools.get_uniform_range(params['root_node']['board'])
range2 = card_tools.get_uniform_range(params['root_node']['board'])
filling.fill_uniform(tree)
starting_ranges = arguments.Tensor(game_settings.player_count, game_settings.card_count)
starting_ranges[0].copy_(range1)
starting_ranges[1].copy_(range2)
table_sl.model.eval()
# self.dfs_fill_table(tree, table_sl,builder)
self.dfs_fill_strategy(table_sl,tree, builder)
tree_values = TreeValues()
tree_values.compute_values(tree, starting_ranges)
print('Exploitability: ' + str(tree.exploitability.item()) + '[chips]' )
return tree.exploitability.item()
import torch
import numpy as np
import Settings.arguments as arguments
import Settings.constants as constants
import Settings.game_settings as game_settings
from itertools import count
from nn.env import Env
from nn.dqn import DQN
from nn.dqn import DQNOptim
from nn.table_sl import TableSL
from nn.Q_learning import QLearning
from nn.state import GameState
from Tree.tree_builder import PokerTreeBuilder
from Tree.Tests.test_tree_values import ValuesTester
from collections import namedtuple
builder = PokerTreeBuilder()
num_episodes = 10
env = Env()
value_tester = ValuesTester()
action = np.arange(5)
Agent = namedtuple('Agent', ['rl', 'sl'])
agent0 = Agent(rl=QLearning(action), sl=TableSL())
agent1 = Agent(rl=QLearning(action), sl=TableSL())
table_sl = agent0.sl
agents = [agent0, agent1]
def load_model(dqn_optim, iter_time):
def __init__(self, verbose=0):
self.tree_builder = PokerTreeBuilder()
self.verbose = verbose
