def _get_environment(self):
"""Observe the environment"""
if not self.done:
self._get_legal_moves()
self.observation = None
self.reward = 0
self.info = None
self.community_data = CommunityData(len(self.players))
self.community_data.community_pot = self.community_pot / (
self.big_blind * 100)
self.community_data.current_round_pot = self.current_round_pot / (
self.big_blind * 100)
self.community_data.small_blind = self.small_blind
self.community_data.big_blind = self.big_blind
self.community_data.stage[np.minimum(self.stage.value, 3)] = 1 # pylint: disable= invalid-sequence-index
self.community_data.legal_moves = [
action in self.legal_moves for action in Action
]
# self.cummunity_data.active_players
self.player_data = PlayerData()
self.player_data.stack = [
player.stack / (self.big_blind * 100) for player in self.players
]
if not self.current_player: # game over
self.current_player = self.players[self.winner_ix]
self.player_data.position = self.current_player.seat
self.current_player.equity_alive = self.get_equity(
set(self.current_player.cards), set(self.table_cards),
sum(self.player_cycle.alive), 1000)
self.player_data.equity_to_river_alive = self.current_player.equity_alive
arr1 = np.array(list(flatten(self.player_data.__dict__.values())))
arr2 = np.array(list(flatten(self.community_data.__dict__.values())))
arr3 = np.array([
list(flatten(sd.__dict__.values())) for sd in self.stage_data
]).flatten()
# arr_legal_only = np.array(self.community_data.legal_moves).flatten()
self.array_everything = np.concatenate([arr1, arr2, arr3]).flatten()
self.observation = self.array_everything
self._get_legal_moves()
self.info = {
'player_data': self.player_data.__dict__,
'community_data': self.community_data.__dict__,
'stage_data': [stage.__dict__ for stage in self.stage_data],
'legal_moves': self.legal_moves
}
self.observation_space = self.array_everything.shape
if self.render_switch:
self.render()
def _get_environment(self):
"""Observe the environment"""
if not self.done:
self._get_legal_moves()
self.observation = None
self.reward = None
self.info = None
self.community_data = CommunityData(len(self.players))
self.community_data.community_pot = self.community_pot
self.community_data.current_round_pot = self.current_round_pot
self.community_data.small_blind = self.small_blind
self.community_data.big_blind = self.big_blind
self.community_data.stage[np.minimum(self.stage.value, 3)] = 1
# self.cummunity_data.active_players
self.player_data = PlayerData()
self.player_data.stack = [player.stack for player in self.players]
if not self.current_player: # game over
self.current_player = self.players[self.winner_ix]
self.player_data.position = self.current_player.seat
self.current_player.equity_alive = get_equity(
self.current_player.cards, self.table_cards,
sum(self.player_cycle.alive))
self.player_data.equity_to_river_alive = self.current_player.equity_alive
arr1 = np.array(list(flatten(self.player_data.__dict__.values())))
arr2 = np.array(list(flatten(self.community_data.__dict__.values())))
arr3 = np.array([
list(flatten(sd.__dict__.values())) for sd in self.stage_data
]).flatten()
self.array_everything = np.concatenate([arr1, arr2, arr3]).flatten()
self.observation = {
'array_everything': self.array_everything,
'player_data': self.player_data,
'community_data': self.community_data,
'stage_data': self.stage_data
}
self._get_legal_moves()
self.reward = self.current_player.stack + self.player_data.equity_to_river_alive * self.community_pot
if self.render_switch:
self.render()
def _get_environment(self):
"""Observe the environment"""
if not self.done:
self._get_legal_moves()
self.observation = None
self.reward = 0
self.info = None
self.community_data = CommunityData(len(self.players))
self.community_data.community_pot = self.community_pot / (
self.big_blind * 100)
self.community_data.current_round_pot = self.current_round_pot / (
self.big_blind * 100)
self.community_data.small_blind = self.small_blind
self.community_data.big_blind = self.big_blind
self.community_data.stage[np.minimum(self.stage.value, 3)] = 1
self.community_data.game_stage = self.stage.value
self.community_data.legal_moves = [
action in self.legal_moves for action in Action
]
self.community_data.dealer_position = self.player_cycle.dealer_idx
self.community_data.active_players = self.player_cycle.alive
self.community_data.min_call = self.min_call / (self.big_blind * 10000)
self.player_data = PlayerData()
self.player_data.stack = [
player.stack / (self.big_blind * 100) for player in self.players
]
if not self.current_player: # game over
self.current_player = self.players[self.winner_ix]
self.player_data.position = self.current_player.seat
self.current_player.equity_alive = get_equity(
self.current_player.cards, self.table_cards,
sum(self.player_cycle.alive))
self.player_data.equity_to_river_alive = self.current_player.equity_alive
self.player_data.stack_amount = self.current_player.stack
self.player_data.first_decision = (
1 if self.current_player.last_action_in_stage == '' else 0)
self.player_data.hand_rank = self.get_rank(self.stage,
self.current_player.cards,
self.table_cards)
arr1 = np.array(list(flatten(self.player_data.__dict__.values())))
arr2 = np.array(list(flatten(self.community_data.__dict__.values())))
arr3 = np.array([
list(flatten(sd.__dict__.values())) for sd in self.stage_data
]).flatten()
# arr_legal_only = np.array(self.community_data.legal_moves).flatten()
self.array_everything = np.concatenate([arr1, arr2, arr3]).flatten()
self.observation = self.array_everything
self.info = {
'player_data': self.player_data.__dict__,
'community_data': self.community_data.__dict__,
'stage_data': [stage.__dict__ for stage in self.stage_data]
}
self._get_legal_moves()
self.observation_space = self.array_everything.shape
if self.render_switch:
self.render()
