def get_action_policy(player_infos, community_infos, community_cards, env, _round, n_seats, state, policy, villain): player_actions = None current_player = community_infos[-3] player_object = env._player_dict[current_player] to_call = community_infos[-1] stack, hand_rank, played_this_round, betting, lastsidepot = player_infos[current_player-1] if current_player is 2 else player_infos[current_player] player_object.he.set_community_cards(community_cards, _round) if _round is not "Preflop": # preflop already evaluated player_object.he.evaluate(_round) range_structure = utilities.fill_range_structure(_round, player_object) utilities.assign_evals_player(player_object, _round, env) if(current_player == 0): # learner move probs = policy(state) choice = np.random.choice(np.arange(len(probs)), p=probs) best_nonlearning_action = player_object.choose_action(_round, range_structure, env) # Doesn't use player_actions = holdem.safe_actions(to_call, community_infos, villain_choice=None, n_seats=n_seats, choice=choice, player_o = player_object, best_nonlearning_action=best_nonlearning_action) else: # bot move if villain == "CallChump": player_actions = utilities.safe_actions_call_bot(community_infos, villain_choice=None, n_seats=n_seats) else: villain_choice = player_object.choose_action(_round, range_structure, env) player_actions = holdem.safe_actions(to_call, community_infos, villain_choice, n_seats=n_seats, choice=None, player_o = player_object) return player_actions
def get_action_policy(player_infos, community_infos, community_cards, env,
_round, n_seats, state, policy):
player_actions = None
current_player = community_infos[-3]
player_object = env._player_dict[current_player]
to_call = community_infos[-1]
stack, hand_rank, played_this_round, betting, lastsidepot = player_infos[
current_player -
1] if current_player is 2 else player_infos[current_player]
player_object.he.set_community_cards(community_cards, _round)
if _round is not "Preflop": # preflop already evaluated
player_object.he.evaluate(_round)
range_structure = utilities.fill_range_structure(_round, player_object)
utilities.assign_evals_player(player_object, _round, env)
if (current_player == 0): # learner move
probs = policy(state)
choice = np.random.choice(np.arange(len(probs)), p=probs)
best_nonlearning_action = player_object.choose_action(
_round, range_structure, env) # Doesn't use
player_actions = holdem.safe_actions(
to_call,
community_infos,
villain_choice=None,
n_seats=n_seats,
choice=choice,
player_o=player_object,
best_nonlearning_action=best_nonlearning_action)
else: # bot move
if villain == "CallChump":
player_actions = utilities.safe_actions_call_bot(
community_infos, villain_choice=None, n_seats=n_seats)
else:
villain_choice = player_object.choose_action(
_round, range_structure, env)
player_actions = holdem.safe_actions(to_call,
community_infos,
villain_choice,
n_seats=n_seats,
choice=None,
player_o=player_object)
this_lr = (sum(p == player_object.get_seat()
for p, v in env.level_raises.items()))
if env.highest_in_LR()[1] is not player_object.get_seat(
) and env.highest_in_LR()[0] > this_lr:
prohibit_action(player_actions, current_player, ban=[0, 0])
# a,b = env.highest_in_LR()
# print(player_actions)
# which_action = player_object.choose_action(_round, range_structure, env)
# player_actions = holdem.safe_actions(community_infos, which_action, n_seats=n_seats, choice=None)
return player_actions
def act(self, state, player_infos, community_infos, community_cards, env,
_round, n_seats, state_set, policy):
if np.random.rand() <= self.epsilon:
action = get_action_policy(player_infos, community_infos,
community_cards, env, _round, n_seats,
state_set, policy, villain)
return action
act_values = self.model.predict(
state
) # if not acting according to safe_strategy, predict reward value based on current state
predicted_action = np.argmax(act_values[0])
env.learner_bot.he.set_community_cards(community_cards, _round)
range_structure = utilities.fill_range_structure(
_round, env.learner_bot)
utilities.assign_evals_player(env.learner_bot, _round, env)
choice = None
if predicted_action == 0:
choice = 1
elif predicted_action == 1:
total_bet = env._tocall + env._bigblind - env.villain.currentbet
choice = (2, total_bet)
elif predicted_action == 2:
choice = 3
predicted_action = holdem.safe_actions(community_infos[-1],
community_infos,
villain_choice=None,
n_seats=n_seats,
choice=choice,
player_o=env.learner_bot)
return predicted_action # pick the action that will give the highest reward (i.e., go left or right?)
def get_guest_action(self):
action = self.guest_action
action = self.parse_action(action)
player_actions = holdem.safe_actions(self.community_infos[-1],
self.community_infos,
action,
n_seats=env.n_seats,
choice=None,
player_o=self.p2)
return player_actions
def play_out_hand(env, n_seats):
# reset environment, gather relevant observations
(player_states, (community_infos, community_cards)) = env.reset()
(player_infos, player_hands) = zip(*player_states)
# display the table, cards and all
env.render(mode='human')
terminal = False
while not terminal:
# play safe actions, check when noone else has raised, call when raised.
actions = holdem.safe_actions(community_infos, n_seats=n_seats)
(player_states, (community_infos, community_cards)), rews, terminal, info = env.step(actions)
env.render(mode='human')
