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()
def resolve_first_node(self): ''' Solves a depth-limited lookahead from the first node of the game to get opponent counterfactual values. The cfvs are stored in the field `starting_cfvs_p1`. Because this is the first node of the game, exact ranges are known for both players, so opponent cfvs are not necessary for solving. ''' self.first_node_resolving = Resolving() first_node = Node() first_node.board = np.zeros([]) first_node.street = 1 first_node.current_player = constants.players.P1 first_node.bets = np.array([arguments.ante, arguments.ante], dtype=arguments.dtype) # create the starting ranges player_range = card_tools.get_uniform_range(first_node.board) opponent_range = card_tools.get_uniform_range(first_node.board) # create re-solving and re-solve the first node self.first_node_resolving = Resolving() self.first_node_resolving.resolve_first_node(first_node, player_range, opponent_range) # store the initial CFVs self.starting_cfvs_p1 = self.first_node_resolving.get_root_cfv()
def __init__(self): ''' Does a depth-limited solve of the game's first node. ''' self.starting_player_range = card_tools.get_uniform_range(np.zeros([]))
builder = PokerTreeBuilder()
params = {}
params['root_node'] = {}
params['root_node']['board'] = card_to_string.string_to_board('Ks')
params['root_node']['street'] = 1
params['root_node']['current_player'] = constants.players.P1
params['root_node']['bets'] = arguments.Tensor([100, 100])
params['limit_to_street'] = True
tree = builder.build_tree(params)
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(constants.players_count, game_settings.card_count)
starting_ranges[0].copy_(range1)
starting_ranges[1].copy_(range2)
#tree_values = TreeValues()
#tree_values:compute_values(tree, starting_ranges)
#print('Exploitability: ' + tree.exploitability + '[chips]' )
visualiser = TreeVisualiser()
from time import time
PC, CC = constants.players_count, game_settings.card_count
params = TreeParams()
params.root_node = Node()
params.root_node.board = card_to_string.string_to_board('')
params.root_node.street = 1
params.root_node.current_player = constants.players.P1
params.root_node.bets = np.array([100, 100])
tree = tree_builder.build_tree(params)
starting_ranges = np.zeros([PC, CC], dtype=arguments.dtype)
starting_ranges[0] = card_tools.get_uniform_range(params.root_node.board)
starting_ranges[1] = card_tools.get_uniform_range(params.root_node.board)
t0 = time()
tree_cfr = TreeCFR()
tree_cfr.run_cfr(tree, starting_ranges)
print(time() - t0)
tree_values = TreeValues()
tree_values.compute_values(tree, starting_ranges)
print('Exploitability: ' + str(tree.exploitability) + ' [chips]')
print(np.array2string(tree.strategy, suppress_small=True, precision=2))
# tree_visualizer.draw_tree(tree)