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"""
Adapt the Go environment in PettingZoo (https://github.com/Farama-Foundation/PettingZoo) to the BaseEnv interface.
"""
import os
import sys
import numpy as np
import pygame
from ding.envs import BaseEnv, BaseEnvTimestep
from ding.utils import ENV_REGISTRY
from gymnasium import spaces
from pettingzoo.classic.go import coords, go
from pettingzoo.utils.agent_selector import agent_selector
def get_image(path):
from os import path as os_path
import pygame
cwd = os_path.dirname(__file__)
image = pygame.image.load(cwd + '/' + path)
sfc = pygame.Surface(image.get_size(), flags=pygame.SRCALPHA)
sfc.blit(image, (0, 0))
return sfc
@ENV_REGISTRY.register('Go')
class GoEnv(BaseEnv):
def __init__(self, board_size: int = 19, komi: float = 7.5):
# board_size: a int, representing the board size (board has a board_size x board_size shape)
# komi: a float, representing points given to the second player.
self._overwrite_go_global_variables(board_size=board_size)
self._komi = komi
self.agents = ['black_0', 'white_0']
self.num_agents = len(self.agents)
self.possible_agents = self.agents[:]
self.has_reset = False
self.screen = None
self._observation_space = self._convert_to_dict(
[
spaces.Dict(
{
'observation': spaces.Box(low=0, high=1, shape=(self._N, self._N, 17), dtype=bool),
'action_mask': spaces.Box(low=0, high=1, shape=((self._N * self._N) + 1, ), dtype=np.int8)
}
) for _ in range(self.num_agents)
]
)
self._action_space = self._convert_to_dict(
[spaces.Discrete(self._N * self._N + 1) for _ in range(self.num_agents)]
)
self._agent_selector = agent_selector(self.agents)
self.board_history = np.zeros((self._N, self._N, 16), dtype=bool)
def _overwrite_go_global_variables(self, board_size: int):
self._N = board_size
go.N = self._N
go.ALL_COORDS = [(i, j) for i in range(self._N) for j in range(self._N)]
go.EMPTY_BOARD = np.zeros([self._N, self._N], dtype=np.int8)
go.NEIGHBORS = {
(x, y): list(filter(self._check_bounds, [(x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)]))
for x, y in go.ALL_COORDS
}
go.DIAGONALS = {
(x, y): list(filter(self._check_bounds, [(x + 1, y + 1), (x + 1, y - 1), (x - 1, y + 1), (x - 1, y - 1)]))
for x, y in go.ALL_COORDS
}
return
def _check_bounds(self, c):
return 0 <= c[0] < self._N and 0 <= c[1] < self._N
def _encode_player_plane(self, agent):
if agent == self.possible_agents[0]:
return np.zeros([self._N, self._N], dtype=bool)
else:
return np.ones([self._N, self._N], dtype=bool)
def _encode_board_planes(self, agent):
agent_factor = go.BLACK if agent == self.possible_agents[0] else go.WHITE
current_agent_plane_idx = np.where(self._go.board == agent_factor)
opponent_agent_plane_idx = np.where(self._go.board == -agent_factor)
current_agent_plane = np.zeros([self._N, self._N], dtype=bool)
opponent_agent_plane = np.zeros([self._N, self._N], dtype=bool)
current_agent_plane[current_agent_plane_idx] = 1
opponent_agent_plane[opponent_agent_plane_idx] = 1
return current_agent_plane, opponent_agent_plane
def _int_to_name(self, ind):
return self.possible_agents[ind]
def _name_to_int(self, name):
return self.possible_agents.index(name)
def _convert_to_dict(self, list_of_list):
return dict(zip(self.possible_agents, list_of_list))
def _encode_legal_actions(self, actions):
return np.where(actions == 1)[0]
def _encode_rewards(self, result):
return [1, -1] if result == 1 else [-1, 1]
@property
def current_player(self):
return self.current_player_index
@property
def to_play(self):
return self.current_player_index
def reset(self):
self.has_reset = True
self._go = go.Position(board=None, komi=self._komi)
self.agents = self.possible_agents[:]
self._agent_selector.reinit(self.agents)
self.agent_selection = self._agent_selector.reset()
self._cumulative_rewards = self._convert_to_dict(np.array([0.0, 0.0]))
self.rewards = self._convert_to_dict(np.array([0.0, 0.0]))
self.dones = self._convert_to_dict([False for _ in range(self.num_agents)])
self.infos = self._convert_to_dict([{} for _ in range(self.num_agents)])
self.next_legal_moves = self._encode_legal_actions(self._go.all_legal_moves())
self._last_obs = self.observe(self.agents[0])
self.board_history = np.zeros((self._N, self._N, 16), dtype=bool)
self.current_player_index = 0
for agent, reward in self.rewards.items():
self._cumulative_rewards[agent] += reward
agent = self.agent_selection
current_index = self.agents.index(agent)
self.current_player_index = current_index
obs = self.observe(agent)
return obs
def observe(self, agent):
player_plane = self._encode_player_plane(agent)
observation = np.dstack((self.board_history, player_plane))
legal_moves = self.next_legal_moves if agent == self.agent_selection else []
action_mask = np.zeros((self._N * self._N) + 1, 'int8')
for i in legal_moves:
action_mask[i] = 1
return {'observation': observation, 'action_mask': action_mask}
def set_game_result(self, result_val):
for i, name in enumerate(self.agents):
self.dones[name] = True
result_coef = 1 if i == 0 else -1
self.rewards[name] = result_val * result_coef
self.infos[name] = {'legal_moves': []}
def step(self, action):
if self.dones[self.agent_selection]:
return self._was_done_step(action)
self._go = self._go.play_move(coords.from_flat(action))
self._last_obs = self.observe(self.agent_selection)
current_agent_plane, opponent_agent_plane = self._encode_board_planes(self.agent_selection)
self.board_history = np.dstack((current_agent_plane, opponent_agent_plane, self.board_history[:, :, :-2]))
next_player = self._agent_selector.next()
current_agent = next_player # 'black_0', 'white_0'
current_index = self.agents.index(current_agent) # 0, 1
self.current_player_index = current_index
if self._go.is_game_over():
self.dones = self._convert_to_dict([True for _ in range(self.num_agents)])
self.rewards = self._convert_to_dict(self._encode_rewards(self._go.result()))
self.next_legal_moves = [self._N * self._N]
else:
self.next_legal_moves = self._encode_legal_actions(self._go.all_legal_moves())
self.agent_selection = next_player if next_player else self._agent_selector.next()
# self._accumulate_rewards()
for agent, reward in self.rewards.items():
self._cumulative_rewards[agent] += reward
# observation, reward, done, info = env.last()
agent = self.agent_selection
current_index = self.agents.index(agent)
self.current_player_index = current_index
observation = self.observe(agent)
return BaseEnvTimestep(observation, self._cumulative_rewards[agent], self.dones[agent], self.infos[agent])
def legal_actions(self):
pass
def legal_moves(self):
if self._go.is_game_over():
self.dones = self._convert_to_dict([True for _ in range(self.num_agents)])
self.rewards = self._convert_to_dict(self._encode_rewards(self._go.result()))
self.next_legal_moves = [self._N * self._N]
else:
self.next_legal_moves = self._encode_legal_actions(self._go.all_legal_moves())
return self.next_legal_moves
def random_action(self):
action_list = self.legal_moves()
return np.random.choice(action_list)
def bot_action(self):
# TODO
pass
def human_to_action(self):
"""
Overview:
For multiplayer games, ask the user for a legal action
and return the corresponding action number.
Returns:
An integer from the action space.
"""
# print(self.board)
while True:
try:
print(f"Current available actions for the player {self.to_play()} are:{self.legal_moves()}")
choice = int(input(f"Enter the index of next move for the player {self.to_play()}: "))
if choice in self.legal_moves():
break
except KeyboardInterrupt:
sys.exit(0)
except Exception as e:
print("Wrong input, try again")
return choice
def render(self, mode='human'):
screen_width = 1026
screen_height = 1026
if self.screen is None:
if mode == "human":
pygame.init()
self.screen = pygame.display.set_mode((screen_width, screen_height))
else:
self.screen = pygame.Surface((screen_width, screen_height))
if mode == "human":
pygame.event.get()
size = go.N
# Load and scale all of the necessary images
tile_size = (screen_width) / size
black_stone = get_image(os.path.join('../img', 'GoBlackPiece.png'))
black_stone = pygame.transform.scale(black_stone, (int(tile_size * (5 / 6)), int(tile_size * (5 / 6))))
white_stone = get_image(os.path.join('../img', 'GoWhitePiece.png'))
white_stone = pygame.transform.scale(white_stone, (int(tile_size * (5 / 6)), int(tile_size * (5 / 6))))
tile_img = get_image(os.path.join('../img', 'GO_Tile0.png'))
tile_img = pygame.transform.scale(tile_img, ((int(tile_size * (7 / 6))), int(tile_size * (7 / 6))))
# blit board tiles
for i in range(1, size - 1):
for j in range(1, size - 1):
self.screen.blit(tile_img, ((i * (tile_size)), int(j) * (tile_size)))
for i in range(1, 9):
tile_img = get_image(os.path.join('../img', 'GO_Tile' + str(i) + '.png'))
tile_img = pygame.transform.scale(tile_img, ((int(tile_size * (7 / 6))), int(tile_size * (7 / 6))))
for j in range(1, size - 1):
if i == 1:
self.screen.blit(tile_img, (0, int(j) * (tile_size)))
elif i == 2:
self.screen.blit(tile_img, ((int(j) * (tile_size)), 0))
elif i == 3:
self.screen.blit(tile_img, ((size - 1) * (tile_size), int(j) * (tile_size)))
elif i == 4:
self.screen.blit(tile_img, ((int(j) * (tile_size)), (size - 1) * (tile_size)))
if i == 5:
self.screen.blit(tile_img, (0, 0))
elif i == 6:
self.screen.blit(tile_img, ((size - 1) * (tile_size), 0))
elif i == 7:
self.screen.blit(tile_img, ((size - 1) * (tile_size), (size - 1) * (tile_size)))
elif i == 8:
self.screen.blit(tile_img, (0, (size - 1) * (tile_size)))
offset = tile_size * (1 / 6)
# Blit the necessary chips and their positions
for i in range(0, size):
for j in range(0, size):
if self._go.board[i][j] == go.BLACK:
self.screen.blit(black_stone, ((i * (tile_size) + offset), int(j) * (tile_size) + offset))
elif self._go.board[i][j] == go.WHITE:
self.screen.blit(white_stone, ((i * (tile_size) + offset), int(j) * (tile_size) + offset))
if mode == "human":
pygame.display.update()
observation = np.array(pygame.surfarray.pixels3d(self.screen))
return np.transpose(observation, axes=(1, 0, 2)) if mode == "rgb_array" else None
def observation_space(self):
return self.observation_spaces
def action_space(self):
return self._action_space
def seed(self, seed: int, dynamic_seed: bool = True) -> None:
self._seed = seed
self._dynamic_seed = dynamic_seed
np.random.seed(self._seed)
def close(self) -> None:
pass
def __repr__(self) -> str:
return "LightZero Go Env"
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