def test_get_action_probabilities(self):
wrap = NetworkWrapper(24,
4,
ev_embed_dim=16,
bet_embed_dim=64,
device=torch.device("cuda:0"))
infoset = make_dummy_ev_infoset()
valid_mask = torch.ones(4).to(torch.device("cuda:0"))
p = wrap.get_action_probabilities(infoset, valid_mask)
self.assertEqual(p.shape, (4, ))
class DeepCFRPlayer(BasePokerPlayer):
def __init__(self, load_weights_path):
self.wrap = NetworkWrapper(Constants.NUM_STREETS,
Constants.BET_HISTORY_SIZE,
Constants.NUM_ACTIONS,
64,
device=torch.device("cuda:0"))
if load_weights_path is not None:
model_dict = self.wrap.network().state_dict()
model_dict.update(torch.load(load_weights_path))
print("==> Loaded value network weights from {}".format(
load_weights_path))
def declare_action(self, valid_actions, hole_card, round_state):
"""
valid_actions (list of dict) : i.e [{'action': 'fold', 'amount': 0}, {'action': 'call', 'amount': 2}]
hole_card (list of str) : i.e ['H2', 'C8']
round_state (dict) : i.e {'action_histories': {'preflop': [{'add_amount': 1, 'action': 'SMALLBLIND', 'amount': 1, 'uuid': 'lgwgyegccmfobokqugvwud'}, {'add_amount': 1, 'action': 'BIGBLIND', 'amount': 2, 'uuid': 'jkuneswljvhvmhmcopbpmj'}]}, 'community_card': [], 'round_count': 1, 'big_blind_pos': 0, 'seats': [{'name': 'P1', 'stack': 98, 'uuid': 'jkuneswljvhvmhmcopbpmj', 'state': 'participating'}, {'name': 'P2', 'stack': 99, 'uuid': 'lgwgyegccmfobokqugvwud', 'state': 'participating'}], 'street': 'preflop', 'pot': {'main': {'amount': 3}, 'side': []}, 'next_player': 1, 'small_blind_pos': 1, 'dealer_btn': 0, 'small_blind_amount': 1}
"""
pot_size = round_state["pot"]["main"]["amount"]
actions, mask = generate_actions(valid_actions, pot_size)
infoset = make_infoset_helper(hole_card, round_state)
action_probs = self.wrap.get_action_probabilities(infoset, mask)
# If no predicted actions are valid, choose one of the valid actions at random.
action_probs *= mask
if (action_probs.sum() <= 0):
print("WARNING: Had to choose a random valid action")
action_probs = mask / mask.sum()
action, amount = actions[torch.multinomial(action_probs, 1).item()]
return action, amount
def receive_game_start_message(self, game_info):
pass
def receive_round_start_message(self, round_count, hole_card, seats):
pass
def receive_street_start_message(self, street, round_state):
pass
def receive_game_update_message(self, action, round_state):
pass
def receive_round_result_message(self, winners, hand_info, round_state):
pass