def main(): robot = Robot((18,1), True) #robot.set_speed(100) #_real_map = Arena(robot) #ArenaUtils.load_arena_from_file(_real_map, 'map/SampleWeek11.txt') _explore_map = Arena(robot) _explore_map.set_allunexplored() CommMgr.connect() _explore = Exploration(_explore_map, robot, 300, 3600) _explore.run() CommMgr.close()
def fp():
robot = Robot((18, 1), True)
_real_map = Arena(robot)
ArenaUtils.load_arena_from_file(_real_map, 'map/17_week10.txt')
print('Awaiting FP_START')
while True:
_command = CommMgr.recv()
if _command == 'FP_START':
CommMgr.send('X', CommMgr.ARDUINO)
break
_go_to_wp_goal = FastestPath(_real_map, robot)
_status = _go_to_wp_goal.do_fastest_path_wp_goal(
(7, 10), ArenaConstant.GOAL_POS.value)
def explore():
robot = Robot((18, 1), True)
_explore_map = Arena(robot)
_explore_map.set_allunexplored()
_explore = Exploration(_explore_map, robot, 300, 3600)
_explore.run()
import gin
from gym_splendor_code.envs.mechanics.abstract_observation import DeterministicObservation
from gym_splendor_code.envs.mechanics.state import State
from nn_models.architectures.average_pool_v0 import StateEncoder, ValueRegressor, IdentityTransformer
gin.parse_config_file(
'/home/tomasz/ML_Research/splendor/gym-splendor/experiments/MCTS_series_1/params.gin'
)
from agents.random_agent import RandomAgent
from agents.single_mcts_agent import SingleMCTSAgent
from arena.arena import Arena
from monte_carlo_tree_search.evaluation_policies.value_evaluator_nn import ValueEvaluator
from monte_carlo_tree_search.mcts_algorithms.single_process.single_mcts import SingleMCTS
arek = Arena()
a1 = RandomAgent()
a2 = SingleMCTSAgent(5, ValueEvaluator(), 0.6, True, True)
#
results = arek.run_one_duel('deterministic', [a1, a2])
# state1 = State()
# fufu = SingleMCTS(5, 0.6, ValueEvaluator())
# fufu.create_root(DeterministicObservation(state1))
# fufu.run_mcts_pass()
from arena.arena import Arena
from pygame.locals import *
from arena.wall_initialiser import initialise_walls
from arena.boy_initialiser import initialise_boys
from brains.pathfinding.grid import BackgroundGrid
import pygame
SCREENRECT = Rect(0, 0, 640, 640)
pygame.init()
winstyle = 0
bestdepth = pygame.display.mode_ok(SCREENRECT.size, winstyle, 32)
screen = pygame.display.set_mode((640, 640), winstyle, bestdepth)
arena = Arena(SCREENRECT)
method = 'steering'
initialise_walls(arena, method)
initialise_boys(arena, method)
clock = pygame.time.Clock()
def main(screen, arena):
while 1:
for event in pygame.event.get():
if event.type == QUIT:
return
screen.fill((30, 30, 30))
arena.update_screen_objects(screen)
class StateEncoder(AbstractModel):
def __init__(self,
gems_encoder_dim : int = None,
price_encoder_dim : int = None,
profit_encoder_dim : int = None,
cards_points_dim: int = None,
cards_dense1_dim: int = None,
cards_dense2_dim: int = None,
board_nobles_dense1_dim : int = None,
board_nobles_dense2_dim : int = None,
full_board_dense1_dim: int = None,
full_board_dense2_dim: int = None,
player_points_dim: int = None,
player_nobles_dim: int = None,
full_player_dense1_dim: int = None,
full_player_dense2_dim: int = None,
final_layer= None,
data_transformer = None,
network_name: str = None
):
super().__init__()
self.vectorizer = Vectorizer()
self.final_layer = final_layer
self.data_transformer = data_transformer
self.params['data transormation'] = self.data_transformer.name
self.params['final layer name'] = self.final_layer.name
self.params['gems_encoder_dim'] = gems_encoder_dim
self.params['gems_encoder_dim'] = gems_encoder_dim
self.params['price_encoder_dim'] = price_encoder_dim
self.params['profit_encoder_dim'] = profit_encoder_dim
self.params['cards_points_dim'] = cards_points_dim
self.params['cards_dense1_dim'] = cards_dense1_dim
self.params['cards_dense2_dim'] = cards_dense2_dim
self.params['board_nobles_dense1_dim'] = board_nobles_dense1_dim
self.params['board_nobles_dense2_dim'] = board_nobles_dense2_dim
self.params['full_board_dense1_dim']= full_board_dense1_dim
self.params['full_board_dense2_dim'] = full_board_dense2_dim
self.params['player_points_dim'] = player_points_dim
self.params['player_nobles_dim'] = player_nobles_dim
self.params['full_player_dense1_dim'] = full_player_dense1_dim
self.params['full_player_dense2_dim']= full_player_dense2_dim
self.arena = Arena()
self.network_agent = ValueNNAgent(self)
self.easy_opp = RandomAgent(distribution='first_buy')
self.medium_opp = GreedyAgentBoost()
self.hard_opp = MinMaxAgent()
self.neptune_monitor = NeptuneMonitor()
self.network_name = network_name
self.gems_encoder = GemsEncoder(gems_encoder_dim)
self.price_encoder = PriceEncoder(price_encoder_dim)
self.board_encoder = BoardEncoder(self.gems_encoder,
ManyNoblesEncoder(price_encoder_dim,
board_nobles_dense1_dim,
board_nobles_dense2_dim),
ManyCardsEncoder(MAX_CARDS_ON_BORD,
profit_encoder_dim,
price_encoder_dim,
cards_points_dim,
cards_dense1_dim,
cards_dense2_dim
),
full_board_dense1_dim,
full_board_dense2_dim)
self.player_encoder = PlayerEncoder(self.gems_encoder,
self.price_encoder,
ManyCardsEncoder(MAX_RESERVED_CARDS,
profit_encoder_dim,
price_encoder_dim,
cards_points_dim,
cards_dense1_dim,
cards_dense2_dim
),
player_points_dim,
player_nobles_dim,
full_player_dense1_dim,
full_player_dense2_dim)
active_player_input = PlayersInputGenerator('active_').inputs
other_player_input = PlayersInputGenerator('other_').inputs
board_input = self.board_encoder.inputs
self.inputs = board_input + active_player_input + other_player_input
board_encoded = self.board_encoder(board_input)
active_player_encoded = self.player_encoder(active_player_input)
other_player_encoded = self.player_encoder(other_player_input)
full_state = Concatenate(axis=-1)([board_encoded, active_player_encoded, other_player_encoded])
full_state = Dense(full_player_dense1_dim, activation='relu')(full_state)
final_state = Dense(full_player_dense2_dim, activation='relu')(full_state)
result = self.final_layer(final_state)
self.layer = Model(inputs = self.inputs, outputs = final_state, name = 'full_state_splendor_estimator')
self.network = Model(inputs = self.inputs, outputs = result, name = 'full_state_splendor_estimator')
self.network.compile(Adam(), loss='mean_squared_error')
self.params['Model name'] = 'Average pooling model'
self.params['optimizer_name'] = 'Adam'
def get_value(self, state):
prediciton = self.network.predict(self.vectorizer.state_to_input(state))
return self.final_layer.get_value(prediciton)
def train_on_mcts_data(self, data_frame, train_epochs:int):
X = data_frame['state']
Y = data_frame['mcts_value']
X = self.vectorizer.many_states_to_input(X)
Y = self.data_transformer.transform_array(Y)
fit_history = self.network.fit(X, Y, epochs=train_epochs)
return fit_history
def train_network_on_many_sets(self, train_dir=None, validation_file=None, epochs=None, batch_size=None,
test_games=1):
assert self.network is not None, 'You must create network before training'
with open(validation_file, 'rb') as f:
X_val, Y_val = pickle.load(f)
X_val = self.vectorizer.many_states_to_input(X_val)
Y_val = self.data_transformer.transform_array(Y_val)
self.neptune_monitor.reset_epoch_counter()
file1, file2 = self.gather_data_info(train_dir, validation_file)
self.start_neptune_experiment(experiment_name=self.network_name, description='Training avg_pool arch network',
neptune_monitor=self.neptune_monitor)
self.neptune_monitor.log_histograms(file1, file2)
files_for_training = os.listdir(train_dir)
for epoch in range(epochs):
print(f'\n Epoch {epoch}: \n')
file_epoch = epoch % len(files_for_training)
X, Y = load_data_for_model(os.path.join(train_dir, files_for_training[file_epoch]))
X = self.vectorizer.many_states_to_input(X)
Y = self.data_transformer.transform_array(Y)
self.network.fit(x=X, y=Y, epochs=1, batch_size=batch_size,
validation_data=(X_val, Y_val),
callbacks=[self.neptune_monitor])
del X
del Y
neptune.stop()
def dump_weights(self, file_name):
self.network.save_weights(file_name)
def load_weights(self, file_name):
self.network.load_weights(file_name)
def gather_data_info(self, train_dir, validation_file):
list_of_files = os.listdir(train_dir)
example_file = list_of_files[0]
with open(os.path.join(train_dir, example_file), 'rb') as f1:
_, Y_ex = pickle.load(f1)
with open(validation_file, 'rb') as f2:
_, Y_val = pickle.load(f2)
self.params['train set size'] = len(Y_ex)
self.params['valid set size'] = len(Y_val)
file1 = os.path.join('temp', 'train_hist.png')
file2 = os.path.join('temp', 'valid_hist.png')
Y_ex = self.data_transformer.transform_array(Y_ex)
Y_val = self.data_transformer.transform_array(Y_val)
plt.hist(Y_ex, bins=100)
plt.savefig(file1)
plt.clf()
plt.hist(Y_val, bins=100)
plt.savefig(file2)
return file1, file2
def check_performance(self, n_games, opponents):
performance_results = {}
if 'easy' in opponents:
easy_results = self.arena.run_many_duels('deterministic', [self.network_agent, self.easy_opp], n_games,
shuffle_agents=True)
_, _, easy_win_rate = easy_results.return_stats()
performance_results['easy'] = easy_win_rate / n_games
if 'medium' in opponents:
medium_results = self.arena.run_many_duels('deterministic', [self.network_agent, self.medium_opp], n_games,
shuffle_agents=True)
_, _, medium_win_rate = medium_results.return_stats()
performance_results['medium'] = medium_win_rate / n_games
if 'hard' in opponents:
hard_results = self.arena.run_many_duels('deterministic', [self.network_agent, self.hard_opp], n_games,
shuffle_agents=True)
_, _, hard_win_rate = hard_results.return_stats()
performance_results['hard'] = hard_win_rate / n_games
return performance_results
def run_test(self, n_games):
results = self.check_performance(n_games, ['easy'])
self.neptune_monitor.log_win_rates(['easy'], results)
def evaluate_fixed_states(self):
results = [self.get_value(f_state) for f_state in list_of_fixes_states]
self.neptune_monitor.log_state_values(results)
def init_objects(self, robot, map_file):
self._robot = copy.deepcopy(robot)
self._real_map = Arena(self._robot)
ArenaUtils.load_arena_from_file(self._real_map, map_file)
self._explore_map = Arena(self._robot)
self._explore_map.set_allunexplored()
self.BLOCK_SIZE = 30
self._waypoint = None
# Simulator Main Window
self._arena_size = (ArenaConstant.ARENA_COL.value * self.BLOCK_SIZE,
ArenaConstant.ARENA_ROW.value * self.BLOCK_SIZE)
#self._screen = pygame.display.set_mode((self._arena_size[0] * 2, self._arena_size[1]))
self._screen = pygame.display.set_mode(
(self._arena_size[0], self._arena_size[1]))
self._screen.fill((0, 0, 0))
# Simulator Background
self._background = pygame.Surface(self._arena_size)
self._background = self._background.convert()
self._background.fill((169, 169, 169))
# Simulator Alerts
# self._alert = pygame.Surface((9 * self.BLOCK_SIZE, 3 * self.BLOCK_SIZE))
# self._alert = self._alert.convert()
# self._alert.fill((255,255,255))
# pygame.draw.rect(self._alert, (0,0,0), (0, 0, 9 * self.BLOCK_SIZE, 3 * self.BLOCK_SIZE), 1)
# Simulator Background
self._menu = pygame.Surface(self._arena_size)
self._menu = self._menu.convert()
self._menu.fill((255, 255, 255))
# Prepare Background and Arena
for row in range(ArenaConstant.ARENA_ROW.value):
for col in range(ArenaConstant.ARENA_COL.value):
if row == 17 and col == 0:
pygame.draw.rect(
self._background, (0, 100, 0),
(col * self.BLOCK_SIZE, row * self.BLOCK_SIZE,
self.BLOCK_SIZE * 3, self.BLOCK_SIZE * 3))
if row == 0 and col == 12:
pygame.draw.rect(
self._background, (255, 140, 0),
(col * self.BLOCK_SIZE, row * self.BLOCK_SIZE,
self.BLOCK_SIZE * 3, self.BLOCK_SIZE * 3))
pygame.draw.rect(self._background, (0, 0, 0),
(col * self.BLOCK_SIZE, row * self.BLOCK_SIZE,
self.BLOCK_SIZE, self.BLOCK_SIZE), 1)
# Simulator Menu
# self._large_text = pygame.font.Font('freesansbold.ttf',20)
# self._coverage_ex_menu_surf, self._coverage_ex_menu_rect = self.text_objects("Coverage-Limited Exploration", self._large_text)
# self._coverage_ex_menu_rect.center = ((self._arena_size[0] / 2, 3 * self._arena_size[1] / 7))
# self._coverage_rect = pygame.Rect(self._coverage_ex_menu_rect.left - 15, self._coverage_ex_menu_rect.top - 15, self._coverage_ex_menu_rect.width + self.BLOCK_SIZE, self._coverage_ex_menu_rect.height + self.BLOCK_SIZE)
# pygame.draw.rect(self._menu, (0,0,0), self._coverage_rect, 3)
# self._menu.blit(self._coverage_ex_menu_surf, self._coverage_ex_menu_rect)
# self._ex_menu_surf, self._ex_menu_rect = self.text_objects("Exploration", self._large_text)
# self._ex_menu_rect.center = ((self._arena_size[0] / 2, self._arena_size[1] / 7))
# self._ex_rect = pygame.Rect.copy(self._coverage_rect)
# self._ex_rect.center = self._ex_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._ex_rect, 3)
# self._menu.blit(self._ex_menu_surf, self._ex_menu_rect)
# self._timed_ex_menu_surf, self._timed_ex_menu_rect = self.text_objects("Timed Exploration", self._large_text)
# self._timed_ex_menu_rect.center = ((self._arena_size[0] / 2, 2 * self._arena_size[1] / 7))
# self._timed_rect = pygame.Rect.copy(self._coverage_rect)
# self._timed_rect.center = self._timed_ex_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._timed_rect, 3)
# self._menu.blit(self._timed_ex_menu_surf, self._timed_ex_menu_rect)
# self._wp_menu_surf, self._wp_menu_rect = self.text_objects("Add Waypoint", self._large_text)
# self._wp_menu_rect.center = ((self._arena_size[0] / 2, 4 * self._arena_size[1] / 7))
# self._wp_rect = pygame.Rect.copy(self._coverage_rect)
# self._wp_rect.center = self._wp_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._wp_rect, 3)
# self._menu.blit(self._wp_menu_surf, self._wp_menu_rect)
# self._fp_menu_surf, self._fp_menu_rect = self.text_objects("Fastest Path", self._large_text)
# self._fp_menu_rect.center = ((self._arena_size[0] / 2, 5 * self._arena_size[1] / 7))
# self._fp_rect = pygame.Rect.copy(self._coverage_rect)
# self._fp_rect.center = self._fp_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._fp_rect, 3)
# self._menu.blit(self._fp_menu_surf, self._fp_menu_rect)
# self._mdf_menu_surf, self._mdf_menu_rect = self.text_objects("Generate MDF", self._large_text)
# self._mdf_menu_rect.center = ((self._arena_size[0] / 2, 6 * self._arena_size[1] / 7))
# self._mdf_rect = pygame.Rect.copy(self._coverage_rect)
# self._mdf_rect.center = self._mdf_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._mdf_rect, 3)
# self._menu.blit(self._mdf_menu_surf, self._mdf_menu_rect)
self._screen.blit(self._background, (0, 0))
#self._screen.blit(self._alert, (3 * self.BLOCK_SIZE, 8 * self.BLOCK_SIZE))
self._screen.blit(self._menu,
(ArenaConstant.ARENA_COL.value * self.BLOCK_SIZE, 0))
pygame.display.update()
def __init__(self, environment_id='gym_splendor_code:splendor-v0'):
self.environment_id = environment_id
self.progress_bar = None
self.local_arena = Arena()
main_process = MPI.COMM_WORLD.Get_rank() == 0
do = 1
if do == 1:
moominer = ValueFunctionOptimizer()
time_s = time.time()
val = moominer.eval_metrics(100)
if main_process:
print(f'Time taken = {time.time() - time_s}')
print(f'value = {val}')
if do == 2:
a1 = GreedyAgentBoost()
a2 = ValueFunctionAgent()
arek = ArenaMultiThread()
res = arek.run_many_games('deterministic', [a1, a2], 100)
if main_process:
print(res)
if do == 3:
from agents.random_agent import RandomAgent
from agents.value_function_agent import ValueFunctionAgent
a1 = RandomAgent()
a2 = ValueFunctionAgent()
from arena.arena import Arena
arek = Arena()
arek.run_one_duel('deterministic', [a1, a2], render_game=True)
def __init__(self, alpha):
self.agent = QValueAgent()
self.env = gym_open_ai.make('splendor-v0')
self.weights_token = 'weights_' + str(random.randint(0,1000000)) + '.h5'
self.arena = Arena()
self.alpha = alpha
class ArenaMultiThread:
def __init__(self, environment_id='gym_splendor_code:splendor-v0'):
self.environment_id = environment_id
self.progress_bar = None
self.local_arena = Arena()
def create_progress_bar(self, lenght):
if main_thread and USE_TQDM:
self.progress_bar = tqdm(total=lenght, postfix=None)
def set_progress_bar(self, value):
if main_thread and USE_TQDM:
self.progress_bar.n = min(value, self.progress_bar.total - 1)
self.progress_bar.update()
def start_collecting_states(self):
self.local_arena.start_collecting_states()
def collect_only_from_middle_game(self, n_min_actions, dump_probability):
self.local_arena.collect_only_from_middle_game(n_min_actions,
dump_probability)
def stop_collecting_states(self):
self.local_arena.start_collecting_states()
def dump_collected_states(self, filename, folder):
self.local_arena.dump_collected_states(filename, folder, my_rank)
def return_collected_states(self):
return self.local_arena.collect_states_df
def collected_states_to_csv(self, filename):
self.local_arena.collected_states_to_csv(filename, my_rank)
def one_group_vs_other_duels(self,
mode,
list_of_agents1: List[Agent],
list_of_agents2: List[Agent],
games_per_duel: int,
shuffle: bool = True):
#create all pairs to fightd
all_pairs = list(product(list_of_agents1, list_of_agents2))
pairs_to_duel = [pair for pair in all_pairs if pair[0] != pair[1]]
#create list of jobs:
list_of_jobs = pairs_to_duel * games_per_duel
#calculate jobs per thread:
jobs_per_thread = int(len(list_of_jobs) / n_proc)
remaining_jobs = len(list_of_jobs) % n_proc
#create local arena
local_results = GameStatisticsDuels(list_of_agents1, list_of_agents2)
add_remaining_job = int(my_rank < remaining_jobs)
#create progress bar
self.create_progress_bar(len(list_of_jobs))
for game_id in range(0, jobs_per_thread + add_remaining_job):
if main_thread:
pass
#print(f'game_id = {game_id}')
pair_to_duel = list_of_jobs[game_id * n_proc + my_rank]
if shuffle:
starting_agent_id = random.choice(range(2))
one_game_results = self.local_arena.run_one_duel(
mode, list(pair_to_duel))
local_results.register(one_game_results)
if main_thread:
self.set_progress_bar((game_id + 1) * n_proc)
#gather all results
cumulative_results_unprocessed = comm.gather(local_results, root=0)
if main_thread:
cumulative_results = GameStatisticsDuels(list_of_agents1,
list_of_agents2)
for one_thread_results in cumulative_results_unprocessed:
cumulative_results.register(one_thread_results)
return cumulative_results
def run_many_games(self, mode, list_of_agents, n_games):
return self.one_group_vs_other_duels(mode, [list_of_agents[0]],
[list_of_agents[1]],
games_per_duel=n_games,
shuffle=True)
def all_vs_all(self, mode, list_of_agents, n_games):
return self.one_group_vs_other_duels(mode,
list_of_agents,
list_of_agents,
games_per_duel=n_games)
class QLearningTrainer:
def __init__(self, alpha):
self.agent = QValueAgent()
self.env = gym_open_ai.make('splendor-v0')
self.weights_token = 'weights_' + str(random.randint(0,1000000)) + '.h5'
self.arena = Arena()
self.alpha = alpha
def _set_token(self, token):
self.weights_token = token
def _get_token(self):
return self.weights_token
def _save_weights(self):
self.agent.model.save_weights(self.weights_token)
def _load_weights(self):
self.agent.model.load_weights(self.weights_token)
def new_value_formula(self, old_value, best_value, winner_id, reward, alpha):
if winner_id is not None:
return reward
if winner_id is None:
if old_value and best_value is not None:
return (1-alpha)*old_value + alpha*best_value
else:
return None
def run_one_game_and_collect_data(self, debug_info=True):
there_was_no_action = False
self.agent.train_mode()
last_actual_player_0 = None
last_actual_player_1 = None
last_state_player_0 = None
last_state_player_1 = None
last_action_vec_player_0 = None
last_action_vec_player_1 = None
old_value = None
old_state = None
old_action_vec = None
self.env.reset()
observation = self.env.show_observation('deterministic')
is_done = False
number_of_moves = 0
debug_collected_data = pd.DataFrame(columns=('active_player_id', 'winner_id', 'reward', 'best_value'))
collected_data = pd.DataFrame(columns=('state_as_vector', 'value'))
extra_move_done = False
while not (is_done and extra_move_done) and number_of_moves < MAX_NUMBER_OF_MOVES:
if is_done:
extra_move_done = True
current_state_as_dict = StateAsDict(self.env.current_state_of_the_game)
actual_action, actual_eval, best_eval = self.agent.choose_action(observation, [None])
if actual_action is None:
there_was_no_action = True
break
#print('best value = {}'.format(best_value))
observation, reward, is_done, info = self.env.step('deterministic', actual_action)
previous_player_id = self.env.previous_player_id()
winner_id = info['winner_id']
if previous_player_id == 0:
old_value = last_actual_player_0
old_state = last_state_player_0
old_action_vec = last_action_vec_player_0
if previous_player_id == 1:
old_value = last_actual_player_1
old_state = last_state_player_1
old_action_vec = last_action_vec_player_1
if debug_info:
state_status = old_state.__repr__() if old_state is not None else 'NONE'
state_vector = vectorize_state(old_state) if old_state is not None else 'NONE'
debug_collected_data = debug_collected_data.append({
'state_ex' : state_status,
'state_vec' : state_vector,
'new_value': self.new_value_formula(old_value, best_eval,
winner_id, reward, self.alpha),
'active_player_id' : self.env.previous_player_id(),
'winner_id' : winner_id,
'reward' : reward,
'best_eval' : best_eval,
'actual_eval' : actual_eval,
'old_value': old_value,
'pa_points' : self.env.previous_players_hand().number_of_my_points()},
ignore_index=True)
if old_state is not None:
collected_data = collected_data.append({'state_as_vector' : vectorize_state(old_state),
'action_vector' : old_action_vec,
'value': self.new_value_formula(old_value, best_eval,
winner_id, reward, self.alpha)},
ignore_index=True)
if previous_player_id == 0:
last_actual_player_0 = actual_eval
last_state_player_0 = current_state_as_dict
last_action_vec_player_0 = vectorize_action(actual_action)
if previous_player_id == 1:
last_actual_player_1 = actual_eval
last_state_player_1 = current_state_as_dict
last_action_vec_player_1 = vectorize_action(actual_action)
#let the opponent move:
number_of_moves += 1
if debug_info:
debug_collected_data.to_csv('debug_info.csv')
collected_data = collected_data.iloc[0:]
self.agent.test_mode()
return collected_data
def train_network(self, collected_data, epochs):
#prepare X and Y for training:
self.agent.model.train_model(data_frame=collected_data, epochs=epochs)
def run_test(self, opponent: Agent):
results = self.arena.run_many_duels('deterministic', [self.local_trainer.agent, opponent], n_games=comm.Get_size(),
n_proc_per_agent=1, shuffle=True)
if main_process:
print(results)
def run_training(self, n_iterations, opponent):
if USE_NEPTUNE:
neptune.create_experiment('Q learning alpha = '.format(self.alpha))
experience_replay_buffer = None
for i in range(n_iterations):
collected_data, there_was_no_action = self.run_one_game_and_collect_data(debug_info=True)
if not there_was_no_action:
self.agent.model.train_model(data_frame=collected_data, epochs=1)
if experience_replay_buffer is None:
experience_replay_buffer = collected_data
else:
experience_replay_buffer = experience_replay_buffer.append(collected_data)
#Run test
print('Game number = {}'.format(i))
if i%20 == 0 and i > 0:
self.agent.model.train_model(data_frame=experience_replay_buffer, epochs=2)
if i%100 == 0 and i > 0:
experience_replay_buffer = None
print('Clearing buffer')
if i%10 == 0:
if USE_NEPTUNE:
neptune.send_metric('epsilon', x=self.agent.epsilon)
results = self.arena.run_many_duels('deterministic', [self.agent, opponent], number_of_games=50)
print(results)
if USE_NEPTUNE:
for pair in results.data.keys():
neptune.send_metric(pair[0] + '_wins', x=i, y=results.data[pair].wins)
neptune.send_metric(pair[0] + '_reward', x=i, y=results.data[pair].reward)
neptune.send_metric(pair[0] + '_victory_points', x=i, y=results.data[pair].victory_points)
if USE_NEPTUNE:
neptune.stop()
from agents.random_agent import RandomAgent
from agents.greedy_agent_boost import GreedyAgentBoost
from agents.minmax_agent import MinMaxAgent
from agents.greedysearch_agent import GreedySearchAgent
from arena.arena import Arena
fight_pit = Arena()
# time_profile = cProfile.Profile()
# time_profile.run('fight_pit.run_many_duels([goku, gohan], number_of_games=100)')
# time_profile.dump_stats('optimization1.prof')
n_games = 10
gohan = GreedyAgentBoost(weight = [100,2,2,1,0.1])
print(gohan.name)
goku = RandomAgent(distribution='uniform')
print(fight_pit.run_many_duels("deterministic",[goku, gohan], number_of_games = n_games, shuffle_agents=True))
goku = RandomAgent(distribution='uniform_on_types')
print(fight_pit.run_many_duels([goku, gohan], number_of_games = n_games, shuffle_agents=True))
goku = RandomAgent(distribution = 'first_buy')
print(fight_pit.run_many_duels([goku, gohan], number_of_games = n_games, shuffle_agents=True))
gohan = GreedyAgentBoost(weight = [100,2.5,1.5,1,0.1])
from agents.random_agent import RandomAgent
from agents.greedy_agent_boost import GreedyAgentBoost
from arena.arena import Arena
fight_pit = Arena()
def run_comparison(n_games=1000):
gohan = GreedyAgentBoost()
goku = RandomAgent(distribution='uniform')
print(
fight_pit.run_many_duels([goku, gohan],
number_of_games=n_games,
shuffle_agents=True))
goku = RandomAgent(distribution='uniform_on_types')
print(
fight_pit.run_many_duels([goku, gohan],
number_of_games=n_games,
shuffle_agents=True))
goku = RandomAgent(distribution='first_buy')
print(
fight_pit.run_many_duels([goku, gohan],
number_of_games=n_games,
shuffle_agents=True))
gohan = GreedyAgentBoost(weight=[100, 2.5, 1.5, 1, 0.1])
class MultiArena:
def __init__(self) -> None:
self.env_initialized = False
self.name = 'Multi Process Arena'
self.collect_states_mode = False
self.local_arena = Arena()
def initialize_env(
self,
environment_id: str = 'gym_splendor_code:splendor-deterministic-v0'
):
"""Arena has its private environment to run the game."""
self.env = gym_open_ai.make(environment_id)
# def run_multi_process_self_play(self, mode, agent: Agent, render_game = False):
#
# self.local_arena.run_self_play(mode, agent, render_game=render_game, mpi_communicator=comm)
def run_many_duels(self,
mode,
list_of_agents: List[Agent],
n_games: int,
n_proc_per_agent: int,
shuffle: bool = True):
assert n_games > 0, 'Number of games must be positive.'
assert len(
list_of_agents) == 2, 'This method can run on exactly two agents.'
n_process = comm.Get_size()
my_rank = comm.Get_rank()
n_proc_per_agent = max(min(n_proc_per_agent, n_proc), 1)
n_parallel_games = int(n_process / n_proc_per_agent)
remaining_processes = n_process % n_proc_per_agent
extra_process_per_game = int(remaining_processes / n_parallel_games)
remaining_processes_after_all = remaining_processes % n_parallel_games
colors = []
for i in range(n_parallel_games):
if i < remaining_processes_after_all:
processes_to_add = n_proc_per_agent + extra_process_per_game + 1
colors += [i] * processes_to_add
if i >= remaining_processes_after_all:
processes_to_add = n_proc_per_agent + extra_process_per_game
colors += [i] * processes_to_add
my_color = colors[my_rank]
#set agents colors:
for agent in list_of_agents:
agent.set_color(my_color)
#create communicators:
new_communicator = comm.Split(my_color)
#prepare jobs for each group of processes
n_games_for_one_communicator = int(n_games / n_parallel_games)
remaining_games = n_games % n_parallel_games
if my_color < remaining_games:
my_games = n_games_for_one_communicator + 1
if my_color >= remaining_games:
my_games = n_games_for_one_communicator
local_main = new_communicator.Get_rank() == 0
if local_main:
print('My color = {} I have to take = {} games'.format(
my_color, my_games))
local_results = GameStatisticsDuels(list_of_agents[:1],
list_of_agents[1:])
for _ in range(my_games):
if shuffle:
starting_agent_id = random.choice(range(2))
one_game_results = self.local_arena.run_one_duel(
mode, list_of_agents, mpi_communicator=new_communicator)
if local_main:
local_results.register(one_game_results)
#Gather all results:
combined_results_list = comm.gather(local_results, root=0)
if main_process:
global_results = GameStatisticsDuels(list_of_agents[:1],
list_of_agents[1:])
for local_result in combined_results_list:
global_results.register(local_result)
return global_results
from agents.greedy_agent_boost import GreedyAgentBoost
from agents.random_agent import RandomAgent
from agents.single_mcts_agent import SingleMCTSAgent
from arena.arena import Arena
from monte_carlo_tree_search.evaluation_policies.heura_val import HeuraEvaluator
arek = Arena()
a1 = GreedyAgentBoost()
a2 = SingleMCTSAgent(150, HeuraEvaluator(), 0.4, True, False)
results = arek.run_many_duels('deterministic', [a1, a2], 1, True)
print(results)
def __init__(self) -> None:
self.env_initialized = False
self.name = 'Multi Process Arena'
self.collect_states_mode = False
self.local_arena = Arena()
from agents.random_agent import RandomAgent
from agents.minmax_agent import MinMaxAgent
from agents.greedysearch_agent2 import GreedySearchAgent
from arena.arena import Arena
environment_id = 'gym_splendor_code:splendor-v1'
fight_pit = Arena(environment_id)
goku = RandomAgent(distribution='first_buy')
goku2 = RandomAgent(distribution='uniform')
#gohan = RandomAgent(distribution='uniform_on_types')
#gohan = RandomAgent(distribution='uniform')
#goku = GreedyAgen/t(weight = 0.3)
gohan = GreedySearchAgent(depth=5)
goku = MinMaxAgent(name="MinMax", depth=3)
gohan.name = "g2"
goku.name = "g1"
# profi = cProfile.Profile()
#
# profi.run('(fight_pit.run_many_duels([goku, gohan], number_of_games=50))')
# profi.dump_stats('profi2.prof')
fight_pit.run_one_duel([goku, gohan], render_game=True)
# time_dupa = time.time()
# for i in range(100):
# print(i)
# fight_pit = Arena()
# fight_pit.run_one_duel([goku, gohan], starting_agent_id=0)
# print(time.time() - time_dupa)
def __init__(self,
gems_encoder_dim : int = None,
price_encoder_dim : int = None,
profit_encoder_dim : int = None,
cards_points_dim: int = None,
cards_dense1_dim: int = None,
cards_dense2_dim: int = None,
board_nobles_dense1_dim : int = None,
board_nobles_dense2_dim : int = None,
full_board_dense1_dim: int = None,
full_board_dense2_dim: int = None,
player_points_dim: int = None,
player_nobles_dim: int = None,
full_player_dense1_dim: int = None,
full_player_dense2_dim: int = None,
final_layer= None,
data_transformer = None,
network_name: str = None
):
super().__init__()
self.vectorizer = Vectorizer()
self.final_layer = final_layer
self.data_transformer = data_transformer
self.params['data transormation'] = self.data_transformer.name
self.params['final layer name'] = self.final_layer.name
self.params['gems_encoder_dim'] = gems_encoder_dim
self.params['gems_encoder_dim'] = gems_encoder_dim
self.params['price_encoder_dim'] = price_encoder_dim
self.params['profit_encoder_dim'] = profit_encoder_dim
self.params['cards_points_dim'] = cards_points_dim
self.params['cards_dense1_dim'] = cards_dense1_dim
self.params['cards_dense2_dim'] = cards_dense2_dim
self.params['board_nobles_dense1_dim'] = board_nobles_dense1_dim
self.params['board_nobles_dense2_dim'] = board_nobles_dense2_dim
self.params['full_board_dense1_dim']= full_board_dense1_dim
self.params['full_board_dense2_dim'] = full_board_dense2_dim
self.params['player_points_dim'] = player_points_dim
self.params['player_nobles_dim'] = player_nobles_dim
self.params['full_player_dense1_dim'] = full_player_dense1_dim
self.params['full_player_dense2_dim']= full_player_dense2_dim
self.arena = Arena()
self.network_agent = ValueNNAgent(self)
self.easy_opp = RandomAgent(distribution='first_buy')
self.medium_opp = GreedyAgentBoost()
self.hard_opp = MinMaxAgent()
self.neptune_monitor = NeptuneMonitor()
self.network_name = network_name
self.gems_encoder = GemsEncoder(gems_encoder_dim)
self.price_encoder = PriceEncoder(price_encoder_dim)
self.board_encoder = BoardEncoder(self.gems_encoder,
ManyNoblesEncoder(price_encoder_dim,
board_nobles_dense1_dim,
board_nobles_dense2_dim),
ManyCardsEncoder(MAX_CARDS_ON_BORD,
profit_encoder_dim,
price_encoder_dim,
cards_points_dim,
cards_dense1_dim,
cards_dense2_dim
),
full_board_dense1_dim,
full_board_dense2_dim)
self.player_encoder = PlayerEncoder(self.gems_encoder,
self.price_encoder,
ManyCardsEncoder(MAX_RESERVED_CARDS,
profit_encoder_dim,
price_encoder_dim,
cards_points_dim,
cards_dense1_dim,
cards_dense2_dim
),
player_points_dim,
player_nobles_dim,
full_player_dense1_dim,
full_player_dense2_dim)
active_player_input = PlayersInputGenerator('active_').inputs
other_player_input = PlayersInputGenerator('other_').inputs
board_input = self.board_encoder.inputs
self.inputs = board_input + active_player_input + other_player_input
board_encoded = self.board_encoder(board_input)
active_player_encoded = self.player_encoder(active_player_input)
other_player_encoded = self.player_encoder(other_player_input)
full_state = Concatenate(axis=-1)([board_encoded, active_player_encoded, other_player_encoded])
full_state = Dense(full_player_dense1_dim, activation='relu')(full_state)
final_state = Dense(full_player_dense2_dim, activation='relu')(full_state)
result = self.final_layer(final_state)
self.layer = Model(inputs = self.inputs, outputs = final_state, name = 'full_state_splendor_estimator')
self.network = Model(inputs = self.inputs, outputs = result, name = 'full_state_splendor_estimator')
self.network.compile(Adam(), loss='mean_squared_error')
self.params['Model name'] = 'Average pooling model'
self.params['optimizer_name'] = 'Adam'
class Simulator:
def __init__(self, real_run):
self._real_run = real_run
pygame.init()
def init_objects(self, robot, map_file):
self._robot = copy.deepcopy(robot)
self._real_map = Arena(self._robot)
ArenaUtils.load_arena_from_file(self._real_map, map_file)
self._explore_map = Arena(self._robot)
self._explore_map.set_allunexplored()
self.BLOCK_SIZE = 30
self._waypoint = None
# Simulator Main Window
self._arena_size = (ArenaConstant.ARENA_COL.value * self.BLOCK_SIZE,
ArenaConstant.ARENA_ROW.value * self.BLOCK_SIZE)
#self._screen = pygame.display.set_mode((self._arena_size[0] * 2, self._arena_size[1]))
self._screen = pygame.display.set_mode(
(self._arena_size[0], self._arena_size[1]))
self._screen.fill((0, 0, 0))
# Simulator Background
self._background = pygame.Surface(self._arena_size)
self._background = self._background.convert()
self._background.fill((169, 169, 169))
# Simulator Alerts
# self._alert = pygame.Surface((9 * self.BLOCK_SIZE, 3 * self.BLOCK_SIZE))
# self._alert = self._alert.convert()
# self._alert.fill((255,255,255))
# pygame.draw.rect(self._alert, (0,0,0), (0, 0, 9 * self.BLOCK_SIZE, 3 * self.BLOCK_SIZE), 1)
# Simulator Background
self._menu = pygame.Surface(self._arena_size)
self._menu = self._menu.convert()
self._menu.fill((255, 255, 255))
# Prepare Background and Arena
for row in range(ArenaConstant.ARENA_ROW.value):
for col in range(ArenaConstant.ARENA_COL.value):
if row == 17 and col == 0:
pygame.draw.rect(
self._background, (0, 100, 0),
(col * self.BLOCK_SIZE, row * self.BLOCK_SIZE,
self.BLOCK_SIZE * 3, self.BLOCK_SIZE * 3))
if row == 0 and col == 12:
pygame.draw.rect(
self._background, (255, 140, 0),
(col * self.BLOCK_SIZE, row * self.BLOCK_SIZE,
self.BLOCK_SIZE * 3, self.BLOCK_SIZE * 3))
pygame.draw.rect(self._background, (0, 0, 0),
(col * self.BLOCK_SIZE, row * self.BLOCK_SIZE,
self.BLOCK_SIZE, self.BLOCK_SIZE), 1)
# Simulator Menu
# self._large_text = pygame.font.Font('freesansbold.ttf',20)
# self._coverage_ex_menu_surf, self._coverage_ex_menu_rect = self.text_objects("Coverage-Limited Exploration", self._large_text)
# self._coverage_ex_menu_rect.center = ((self._arena_size[0] / 2, 3 * self._arena_size[1] / 7))
# self._coverage_rect = pygame.Rect(self._coverage_ex_menu_rect.left - 15, self._coverage_ex_menu_rect.top - 15, self._coverage_ex_menu_rect.width + self.BLOCK_SIZE, self._coverage_ex_menu_rect.height + self.BLOCK_SIZE)
# pygame.draw.rect(self._menu, (0,0,0), self._coverage_rect, 3)
# self._menu.blit(self._coverage_ex_menu_surf, self._coverage_ex_menu_rect)
# self._ex_menu_surf, self._ex_menu_rect = self.text_objects("Exploration", self._large_text)
# self._ex_menu_rect.center = ((self._arena_size[0] / 2, self._arena_size[1] / 7))
# self._ex_rect = pygame.Rect.copy(self._coverage_rect)
# self._ex_rect.center = self._ex_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._ex_rect, 3)
# self._menu.blit(self._ex_menu_surf, self._ex_menu_rect)
# self._timed_ex_menu_surf, self._timed_ex_menu_rect = self.text_objects("Timed Exploration", self._large_text)
# self._timed_ex_menu_rect.center = ((self._arena_size[0] / 2, 2 * self._arena_size[1] / 7))
# self._timed_rect = pygame.Rect.copy(self._coverage_rect)
# self._timed_rect.center = self._timed_ex_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._timed_rect, 3)
# self._menu.blit(self._timed_ex_menu_surf, self._timed_ex_menu_rect)
# self._wp_menu_surf, self._wp_menu_rect = self.text_objects("Add Waypoint", self._large_text)
# self._wp_menu_rect.center = ((self._arena_size[0] / 2, 4 * self._arena_size[1] / 7))
# self._wp_rect = pygame.Rect.copy(self._coverage_rect)
# self._wp_rect.center = self._wp_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._wp_rect, 3)
# self._menu.blit(self._wp_menu_surf, self._wp_menu_rect)
# self._fp_menu_surf, self._fp_menu_rect = self.text_objects("Fastest Path", self._large_text)
# self._fp_menu_rect.center = ((self._arena_size[0] / 2, 5 * self._arena_size[1] / 7))
# self._fp_rect = pygame.Rect.copy(self._coverage_rect)
# self._fp_rect.center = self._fp_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._fp_rect, 3)
# self._menu.blit(self._fp_menu_surf, self._fp_menu_rect)
# self._mdf_menu_surf, self._mdf_menu_rect = self.text_objects("Generate MDF", self._large_text)
# self._mdf_menu_rect.center = ((self._arena_size[0] / 2, 6 * self._arena_size[1] / 7))
# self._mdf_rect = pygame.Rect.copy(self._coverage_rect)
# self._mdf_rect.center = self._mdf_menu_rect.center
# pygame.draw.rect(self._menu, (0,0,0), self._mdf_rect, 3)
# self._menu.blit(self._mdf_menu_surf, self._mdf_menu_rect)
self._screen.blit(self._background, (0, 0))
#self._screen.blit(self._alert, (3 * self.BLOCK_SIZE, 8 * self.BLOCK_SIZE))
self._screen.blit(self._menu,
(ArenaConstant.ARENA_COL.value * self.BLOCK_SIZE, 0))
pygame.display.update()
def add_waypoint(self, pos):
self._waypoint = pos
pygame.draw.rect(self._background, (0, 0, 128),
(pos[1] * self.BLOCK_SIZE, pos[0] * self.BLOCK_SIZE,
self.BLOCK_SIZE, self.BLOCK_SIZE))
pygame.draw.rect(self._background, (0, 0, 0),
(pos[1] * self.BLOCK_SIZE, pos[0] * self.BLOCK_SIZE,
self.BLOCK_SIZE, self.BLOCK_SIZE), 1)
self._screen.blit(self._background, (0, 0))
pygame.display.update()
def text_objects(self, text, font):
text_surface = font.render(text, True, (0, 0, 0))
return text_surface, text_surface.get_rect()
def get_key(self):
while 1:
event = pygame.event.poll()
if event.type == KEYDOWN:
return event.key
else:
pass
def display_box(self, screen, message):
"Print a message in a box in the middle of the screen"
fontobject = pygame.font.Font(None, 18)
pygame.draw.rect(screen, (0, 0, 0),
((screen.get_width() / 2) - 100,
(screen.get_height() / 2) - 10, 200, 20), 0)
pygame.draw.rect(screen, (255, 255, 255),
((screen.get_width() / 2) - 102,
(screen.get_height() / 2) - 12, 204, 24), 1)
if len(message) != 0:
screen.blit(fontobject.render(message, 1, (255, 255, 255)),
((screen.get_width() / 2) - 100,
(screen.get_height() / 2) - 10))
pygame.display.flip()
def ask(self, screen, question):
"ask(screen, question) -> answer"
pygame.font.init()
current_string = []
self.display_box(screen, question + ": " + ''.join(current_string))
while 1:
inkey = self.get_key()
if inkey == K_BACKSPACE:
current_string = current_string[0:-1]
elif inkey == K_RETURN:
break
elif inkey == K_MINUS:
current_string.append("_")
elif inkey <= 127:
current_string.append(chr(inkey))
self.display_box(screen, question + ": " + ''.join(current_string))
return ''.join(current_string)
def run_exploration(self, robot, map_file):
self.init_objects(robot, map_file)
while True:
# _mouse = pygame.mouse.get_pos()
# if 450 + self._ex_rect.left < _mouse[0] < 450 + self._ex_rect.right and self._ex_rect.top < _mouse[1] < self._ex_rect.bottom:
# pygame.draw.rect(self._menu, (255,0,0), self._ex_rect, 3)
# else:
# pygame.draw.rect(self._menu, (0,0,0), self._ex_rect, 3)
# if 450 + self._timed_rect.left < _mouse[0] < 450 + self._timed_rect.right and self._timed_rect.top < _mouse[1] < self._timed_rect.bottom:
# pygame.draw.rect(self._menu, (255,0,0), self._timed_rect, 3)
# else:
# pygame.draw.rect(self._menu, (0,0,0), self._timed_rect, 3)
# if 450 + self._coverage_rect.left < _mouse[0] < 450 + self._coverage_rect.right and self._coverage_rect.top < _mouse[1] < self._coverage_rect.bottom:
# pygame.draw.rect(self._menu, (255,0,0), self._coverage_rect, 3)
# else:
# pygame.draw.rect(self._menu, (0,0,0), self._coverage_rect, 3)
# if 450 + self._wp_rect.left < _mouse[0] < 450 + self._wp_rect.right and self._wp_rect.top < _mouse[1] < self._wp_rect.bottom:
# pygame.draw.rect(self._menu, (255,0,0), self._wp_rect, 3)
# else:
# pygame.draw.rect(self._menu, (0,0,0), self._fp_rect, 3)
# if 450 + self._fp_rect.left < _mouse[0] < 450 + self._fp_rect.right and self._fp_rect.top < _mouse[1] < self._fp_rect.bottom:
# pygame.draw.rect(self._menu, (255,0,0), self._fp_rect, 3)
# else:
# pygame.draw.rect(self._menu, (0,0,0), self._fp_rect, 3)
# if 450 + self._mdf_rect.left < _mouse[0] < 450 + self._mdf_rect.right and self._mdf_rect.top < _mouse[1] < self._mdf_rect.bottom:
# pygame.draw.rect(self._menu, (255,0,0), self._mdf_rect, 3)
# else:
# pygame.draw.rect(self._menu, (0,0,0), self._mdf_rect, 3)
# self._screen.blit(self._menu, (ArenaConstant.ARENA_COL.value * self.BLOCK_SIZE,0))
# pygame.display.update()
keys = pygame.key.get_pressed()
for event in pygame.event.get():
if event.type == QUIT or keys[pygame.K_ESCAPE]:
CommMgr.close()
pygame.quit()
sys.exit()
elif keys[pygame.K_RETURN]:
self.init_objects(robot, map_file)
if self._real_run:
CommMgr.connect()
_explore = Exploration(self._explore_map, self._real_map,
self._robot, 300, 3600,
(self._screen, self._background))
_explore.run()
if self._real_run:
CommMgr.close()
