571 lines
20 KiB
Python
571 lines
20 KiB
Python
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from collections import Counter
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import numpy as np
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from douzero.env.game import GameEnv
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Card2Column = {3: 0, 4: 1, 5: 2, 6: 3, 7: 4, 8: 5, 9: 6, 10: 7,
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11: 8, 12: 9, 13: 10, 14: 11, 17: 12}
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NumOnes2Array = {0: np.array([0, 0, 0, 0]),
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1: np.array([1, 0, 0, 0]),
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2: np.array([1, 1, 0, 0]),
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3: np.array([1, 1, 1, 0]),
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4: np.array([1, 1, 1, 1])}
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deck = []
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for i in range(3, 15):
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deck.extend([i for _ in range(4)])
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deck.extend([17 for _ in range(4)])
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deck.extend([20, 30])
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class Env:
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"""
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Doudizhu multi-agent wrapper
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"""
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def __init__(self, objective):
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"""
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Objective is wp/adp. It indicates whether considers
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bomb in reward calculation. Here, we use dummy agents.
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This is because, in the orignial game, the players
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are `in` the game. Here, we want to isolate
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players and environments to have a more gym style
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interface. To achieve this, we use dummy players
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to play. For each move, we tell the corresponding
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dummy player which action to play, then the player
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will perform the actual action in the game engine.
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"""
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self.objective = objective
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# Initialize players
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# We use three dummy player for the target position
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self.players = {}
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for position in ['landlord', 'landlord_up', 'landlord_down']:
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self.players[position] = DummyAgent(position)
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# Initialize the internal environment
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self._env = GameEnv(self.players)
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self.infoset = None
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def reset(self):
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"""
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Every time reset is called, the environment
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will be re-initialized with a new deck of cards.
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This function is usually called when a game is over.
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"""
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self._env.reset()
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# Randomly shuffle the deck
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_deck = deck.copy()
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np.random.shuffle(_deck)
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card_play_data = {'landlord': _deck[:20],
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'landlord_up': _deck[20:37],
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'landlord_down': _deck[37:54],
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'three_landlord_cards': _deck[17:20],
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}
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for key in card_play_data:
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card_play_data[key].sort()
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# Initialize the cards
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self._env.card_play_init(card_play_data)
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self.infoset = self._game_infoset
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return get_obs(self.infoset)
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def step(self, action):
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"""
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Step function takes as input the action, which
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is a list of integers, and output the next obervation,
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reward, and a Boolean variable indicating whether the
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current game is finished. It also returns an empty
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dictionary that is reserved to pass useful information.
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"""
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assert action in self.infoset.legal_actions
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self.players[self._acting_player_position].set_action(action)
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self._env.step()
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self.infoset = self._game_infoset
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done = False
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reward = 0.0
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if self._game_over:
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done = True
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reward = self._get_reward()
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obs = None
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else:
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obs = get_obs(self.infoset)
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return obs, reward, done, {}
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def _get_reward(self):
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"""
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This function is called in the end of each
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game. It returns either 1/-1 for win/loss,
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or ADP, i.e., every bomb will double the score.
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"""
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winner = self._game_winner
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bomb_num = self._game_bomb_num
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if winner == 'landlord':
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if self.objective == 'adp':
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return 2.0 ** bomb_num
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else:
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return 1.0
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else:
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if self.objective == 'adp':
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return -2.0 ** bomb_num
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else:
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return -1.0
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@property
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def _game_infoset(self):
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"""
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Here, inforset is defined as all the information
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in the current situation, incuding the hand cards
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of all the players, all the historical moves, etc.
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That is, it contains perferfect infomation. Later,
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we will use functions to extract the observable
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information from the views of the three players.
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"""
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return self._env.game_infoset
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@property
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def _game_bomb_num(self):
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"""
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The number of bombs played so far. This is used as
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a feature of the neural network and is also used to
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calculate ADP.
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"""
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return self._env.get_bomb_num()
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@property
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def _game_winner(self):
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""" A string of landlord/peasants
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"""
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return self._env.get_winner()
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@property
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def _acting_player_position(self):
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"""
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The player that is active. It can be landlord,
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landlod_down, or landlord_up.
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"""
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return self._env.acting_player_position
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@property
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def _game_over(self):
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""" Returns a Boolean
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"""
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return self._env.game_over
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class DummyAgent(object):
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"""
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Dummy agent is designed to easily interact with the
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game engine. The agent will first be told what action
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to perform. Then the environment will call this agent
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to perform the actual action. This can help us to
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isolate environment and agents towards a gym like
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interface.
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"""
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def __init__(self, position):
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self.position = position
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self.action = None
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def act(self, infoset):
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"""
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Simply return the action that is set previously.
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"""
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assert self.action in infoset.legal_actions
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return self.action
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def set_action(self, action):
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"""
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The environment uses this function to tell
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the dummy agent what to do.
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"""
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self.action = action
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def get_obs(infoset):
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"""
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This function obtains observations with imperfect information
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from the infoset. It has three branches since we encode
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different features for different positions.
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This function will return dictionary named `obs`. It contains
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several fields. These fields will be used to train the model.
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One can play with those features to improve the performance.
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`position` is a string that can be landlord/landlord_down/landlord_up
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`x_batch` is a batch of features (excluding the hisorical moves).
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It also encodes the action feature
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`z_batch` is a batch of features with hisorical moves only.
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`legal_actions` is the legal moves
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`x_no_action`: the features (exluding the hitorical moves and
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the action features). It does not have the batch dim.
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`z`: same as z_batch but not a batch.
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"""
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if infoset.player_position == 'landlord':
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return _get_obs_landlord(infoset)
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elif infoset.player_position == 'landlord_up':
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return _get_obs_landlord_up(infoset)
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elif infoset.player_position == 'landlord_down':
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return _get_obs_landlord_down(infoset)
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else:
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raise ValueError('')
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def _get_one_hot_array(num_left_cards, max_num_cards):
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"""
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A utility function to obtain one-hot endoding
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"""
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one_hot = np.zeros(max_num_cards)
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one_hot[num_left_cards - 1] = 1
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return one_hot
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def _cards2array(list_cards):
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"""
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A utility function that transforms the actions, i.e.,
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A list of integers into card matrix. Here we remove
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the six entries that are always zero and flatten the
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the representations.
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"""
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if len(list_cards) == 0:
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return np.zeros(54, dtype=np.int8)
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matrix = np.zeros([4, 13], dtype=np.int8)
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jokers = np.zeros(2, dtype=np.int8)
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counter = Counter(list_cards)
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for card, num_times in counter.items():
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if card < 20:
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matrix[:, Card2Column[card]] = NumOnes2Array[num_times]
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elif card == 20:
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jokers[0] = 1
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elif card == 30:
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jokers[1] = 1
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return np.concatenate((matrix.flatten('F'), jokers))
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def _action_seq_list2array(action_seq_list):
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"""
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A utility function to encode the historical moves.
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We encode the historical 15 actions. If there is
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no 15 actions, we pad the features with 0. Since
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three moves is a round in DouDizhu, we concatenate
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the representations for each consecutive three moves.
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Finally, we obtain a 5x162 matrix, which will be fed
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into LSTM for encoding.
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"""
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action_seq_array = np.zeros((len(action_seq_list), 54))
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for row, list_cards in enumerate(action_seq_list):
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action_seq_array[row, :] = _cards2array(list_cards)
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action_seq_array = action_seq_array.reshape(5, 162)
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return action_seq_array
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def _process_action_seq(sequence, length=15):
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"""
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A utility function encoding historical moves. We
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encode 15 moves. If there is no 15 moves, we pad
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with zeros.
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"""
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sequence = sequence[-length:].copy()
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if len(sequence) < length:
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empty_sequence = [[] for _ in range(length - len(sequence))]
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empty_sequence.extend(sequence)
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sequence = empty_sequence
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return sequence
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def _get_one_hot_bomb(bomb_num):
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"""
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A utility function to encode the number of bombs
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into one-hot representation.
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"""
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one_hot = np.zeros(15)
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one_hot[bomb_num] = 1
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return one_hot
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def _get_obs_landlord(infoset):
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"""
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Obttain the landlord features. See Table 4 in
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https://arxiv.org/pdf/2106.06135.pdf
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"""
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num_legal_actions = len(infoset.legal_actions)
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my_handcards = _cards2array(infoset.player_hand_cards)
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my_handcards_batch = np.repeat(my_handcards[np.newaxis, :],
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num_legal_actions, axis=0)
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other_handcards = _cards2array(infoset.other_hand_cards)
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other_handcards_batch = np.repeat(other_handcards[np.newaxis, :],
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num_legal_actions, axis=0)
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last_action = _cards2array(infoset.last_move)
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last_action_batch = np.repeat(last_action[np.newaxis, :],
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num_legal_actions, axis=0)
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my_action_batch = np.zeros(my_handcards_batch.shape)
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for j, action in enumerate(infoset.legal_actions):
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my_action_batch[j, :] = _cards2array(action)
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landlord_up_num_cards_left = _get_one_hot_array(
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infoset.num_cards_left_dict['landlord_up'], 17)
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landlord_up_num_cards_left_batch = np.repeat(
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landlord_up_num_cards_left[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_down_num_cards_left = _get_one_hot_array(
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infoset.num_cards_left_dict['landlord_down'], 17)
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landlord_down_num_cards_left_batch = np.repeat(
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landlord_down_num_cards_left[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_up_played_cards = _cards2array(
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infoset.played_cards['landlord_up'])
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landlord_up_played_cards_batch = np.repeat(
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landlord_up_played_cards[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_down_played_cards = _cards2array(
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infoset.played_cards['landlord_down'])
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landlord_down_played_cards_batch = np.repeat(
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landlord_down_played_cards[np.newaxis, :],
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num_legal_actions, axis=0)
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bomb_num = _get_one_hot_bomb(
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infoset.bomb_num)
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bomb_num_batch = np.repeat(
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bomb_num[np.newaxis, :],
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num_legal_actions, axis=0)
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x_batch = np.hstack((my_handcards_batch,
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other_handcards_batch,
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last_action_batch,
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landlord_up_played_cards_batch,
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landlord_down_played_cards_batch,
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landlord_up_num_cards_left_batch,
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landlord_down_num_cards_left_batch,
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bomb_num_batch,
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my_action_batch))
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x_no_action = np.hstack((my_handcards,
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other_handcards,
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last_action,
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landlord_up_played_cards,
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landlord_down_played_cards,
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landlord_up_num_cards_left,
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landlord_down_num_cards_left,
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bomb_num))
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z = _action_seq_list2array(_process_action_seq(
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infoset.card_play_action_seq))
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z_batch = np.repeat(
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z[np.newaxis, :, :],
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num_legal_actions, axis=0)
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obs = {
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'position': 'landlord',
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'x_batch': x_batch.astype(np.float32),
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'z_batch': z_batch.astype(np.float32),
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'legal_actions': infoset.legal_actions,
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'x_no_action': x_no_action.astype(np.int8),
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'z': z.astype(np.int8),
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}
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return obs
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def _get_obs_landlord_up(infoset):
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"""
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Obttain the landlord_up features. See Table 5 in
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https://arxiv.org/pdf/2106.06135.pdf
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"""
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num_legal_actions = len(infoset.legal_actions)
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my_handcards = _cards2array(infoset.player_hand_cards)
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my_handcards_batch = np.repeat(my_handcards[np.newaxis, :],
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num_legal_actions, axis=0)
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other_handcards = _cards2array(infoset.other_hand_cards)
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other_handcards_batch = np.repeat(other_handcards[np.newaxis, :],
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num_legal_actions, axis=0)
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last_action = _cards2array(infoset.last_move)
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last_action_batch = np.repeat(last_action[np.newaxis, :],
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num_legal_actions, axis=0)
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my_action_batch = np.zeros(my_handcards_batch.shape)
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for j, action in enumerate(infoset.legal_actions):
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my_action_batch[j, :] = _cards2array(action)
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last_landlord_action = _cards2array(
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infoset.last_move_dict['landlord'])
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last_landlord_action_batch = np.repeat(
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last_landlord_action[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_num_cards_left = _get_one_hot_array(
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infoset.num_cards_left_dict['landlord'], 20)
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landlord_num_cards_left_batch = np.repeat(
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landlord_num_cards_left[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_played_cards = _cards2array(
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infoset.played_cards['landlord'])
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landlord_played_cards_batch = np.repeat(
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landlord_played_cards[np.newaxis, :],
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num_legal_actions, axis=0)
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last_teammate_action = _cards2array(
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infoset.last_move_dict['landlord_down'])
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last_teammate_action_batch = np.repeat(
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last_teammate_action[np.newaxis, :],
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num_legal_actions, axis=0)
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teammate_num_cards_left = _get_one_hot_array(
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infoset.num_cards_left_dict['landlord_down'], 17)
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teammate_num_cards_left_batch = np.repeat(
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teammate_num_cards_left[np.newaxis, :],
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num_legal_actions, axis=0)
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teammate_played_cards = _cards2array(
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infoset.played_cards['landlord_down'])
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teammate_played_cards_batch = np.repeat(
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teammate_played_cards[np.newaxis, :],
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num_legal_actions, axis=0)
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bomb_num = _get_one_hot_bomb(
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infoset.bomb_num)
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bomb_num_batch = np.repeat(
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bomb_num[np.newaxis, :],
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num_legal_actions, axis=0)
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x_batch = np.hstack((my_handcards_batch,
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other_handcards_batch,
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landlord_played_cards_batch,
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teammate_played_cards_batch,
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last_action_batch,
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last_landlord_action_batch,
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last_teammate_action_batch,
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landlord_num_cards_left_batch,
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teammate_num_cards_left_batch,
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bomb_num_batch,
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my_action_batch))
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x_no_action = np.hstack((my_handcards,
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other_handcards,
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landlord_played_cards,
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teammate_played_cards,
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last_action,
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last_landlord_action,
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last_teammate_action,
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landlord_num_cards_left,
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teammate_num_cards_left,
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bomb_num))
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z = _action_seq_list2array(_process_action_seq(
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infoset.card_play_action_seq))
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z_batch = np.repeat(
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z[np.newaxis, :, :],
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num_legal_actions, axis=0)
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obs = {
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'position': 'landlord_up',
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'x_batch': x_batch.astype(np.float32),
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'z_batch': z_batch.astype(np.float32),
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'legal_actions': infoset.legal_actions,
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'x_no_action': x_no_action.astype(np.int8),
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'z': z.astype(np.int8),
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}
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return obs
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def _get_obs_landlord_down(infoset):
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"""
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Obttain the landlord_down features. See Table 5 in
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https://arxiv.org/pdf/2106.06135.pdf
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"""
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num_legal_actions = len(infoset.legal_actions)
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my_handcards = _cards2array(infoset.player_hand_cards)
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my_handcards_batch = np.repeat(my_handcards[np.newaxis, :],
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num_legal_actions, axis=0)
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other_handcards = _cards2array(infoset.other_hand_cards)
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other_handcards_batch = np.repeat(other_handcards[np.newaxis, :],
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num_legal_actions, axis=0)
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last_action = _cards2array(infoset.last_move)
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last_action_batch = np.repeat(last_action[np.newaxis, :],
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num_legal_actions, axis=0)
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my_action_batch = np.zeros(my_handcards_batch.shape)
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for j, action in enumerate(infoset.legal_actions):
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my_action_batch[j, :] = _cards2array(action)
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last_landlord_action = _cards2array(
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infoset.last_move_dict['landlord'])
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last_landlord_action_batch = np.repeat(
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last_landlord_action[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_num_cards_left = _get_one_hot_array(
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infoset.num_cards_left_dict['landlord'], 20)
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landlord_num_cards_left_batch = np.repeat(
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landlord_num_cards_left[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_played_cards = _cards2array(
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infoset.played_cards['landlord'])
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landlord_played_cards_batch = np.repeat(
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landlord_played_cards[np.newaxis, :],
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num_legal_actions, axis=0)
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last_teammate_action = _cards2array(
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infoset.last_move_dict['landlord_up'])
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last_teammate_action_batch = np.repeat(
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last_teammate_action[np.newaxis, :],
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num_legal_actions, axis=0)
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teammate_num_cards_left = _get_one_hot_array(
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infoset.num_cards_left_dict['landlord_up'], 17)
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teammate_num_cards_left_batch = np.repeat(
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teammate_num_cards_left[np.newaxis, :],
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num_legal_actions, axis=0)
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teammate_played_cards = _cards2array(
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infoset.played_cards['landlord_up'])
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teammate_played_cards_batch = np.repeat(
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teammate_played_cards[np.newaxis, :],
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num_legal_actions, axis=0)
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landlord_played_cards = _cards2array(
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infoset.played_cards['landlord'])
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landlord_played_cards_batch = np.repeat(
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landlord_played_cards[np.newaxis, :],
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num_legal_actions, axis=0)
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bomb_num = _get_one_hot_bomb(
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infoset.bomb_num)
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bomb_num_batch = np.repeat(
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bomb_num[np.newaxis, :],
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num_legal_actions, axis=0)
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x_batch = np.hstack((my_handcards_batch,
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other_handcards_batch,
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landlord_played_cards_batch,
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teammate_played_cards_batch,
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last_action_batch,
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last_landlord_action_batch,
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last_teammate_action_batch,
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landlord_num_cards_left_batch,
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teammate_num_cards_left_batch,
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bomb_num_batch,
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my_action_batch))
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x_no_action = np.hstack((my_handcards,
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other_handcards,
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landlord_played_cards,
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teammate_played_cards,
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last_action,
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last_landlord_action,
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last_teammate_action,
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landlord_num_cards_left,
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teammate_num_cards_left,
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bomb_num))
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z = _action_seq_list2array(_process_action_seq(
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infoset.card_play_action_seq))
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z_batch = np.repeat(
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z[np.newaxis, :, :],
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num_legal_actions, axis=0)
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obs = {
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'position': 'landlord_down',
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'x_batch': x_batch.astype(np.float32),
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'z_batch': z_batch.astype(np.float32),
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'legal_actions': infoset.legal_actions,
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'x_no_action': x_no_action.astype(np.int8),
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'z': z.astype(np.int8),
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}
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return obs
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