def evaluate(agent_profile, agent_new_path, agent_old_path,
games_num, experience_path=None, acceptance_rate=0.6,
verbose=True, debug=False, max_steps=None, self_play_examples_deque=deque([])):
print("Evaluating model with games_num %d and acceptance_rate %f" % (games_num, acceptance_rate))
env_selector = EnvironmentSelector()
agent = env_selector.get_agent(agent_profile)
agent.set_exploration_enabled(False)
agent_profile = env_selector.get_profile(agent_profile)
game = env_selector.get_game(agent_profile.game)
agents = []
for idx in range(game.get_players_num()):
old_agent = agent.clone()
old_agent.load(agent_old_path)
agents.append(old_agent)
agent.load(agent_new_path)
agents[0] = agent
arena_games_results = [0] * len(agents)
arena_examples = []
arena_games_n = int(games_num / game.get_players_num())
world = World()
for jdx in range(game.get_players_num()):
playing_agents = shift_list(agents, jdx)
sess_arena_examples, games_results = world.execute_games(playing_agents,
game,
arena_games_n,
max_game_steps_n=max_steps,
verbose=verbose,
show_every_turn=debug)
games_results = shift_list(games_results, -jdx)
for index in range(len(arena_games_results)):
arena_games_results[index] += games_results[index]
arena_examples.extend(sess_arena_examples)
self_play_examples_deque += arena_examples
if experience_path:
serialize(self_play_examples_deque, experience_path)
cur_rewards = arena_games_results[0]
other_rewards = sum(arena_games_results) - cur_rewards
print("Current agent got rewards: %d\n"
"Total reward across all other agents: %d" % (cur_rewards, other_rewards))
updated = (cur_rewards > other_rewards) >= acceptance_rate
return updated
def reset(self):
self.steps_done = 0
self.terminal = self.steps
self.last_pick = [-1] * self.world.n_cities
self.world = World(self.n_agents, self.n_cities, self.steps, self.conn,
self.tasks, self.cities, self.rewards,
self.destinations, self.budget)
self.remain_budget = self.get_budget()
def generate_self_play(opt_agent_profile,
agent_path,
games_num,
experience_path,
max_steps,
verbose,
debug,
exploration_decay_steps,
optimize_for_inference=False,
self_play_examples_deque=deque([])):
world = World()
env_selector = EnvironmentSelector()
agent = env_selector.get_agent(opt_agent_profile)
agent.load(agent_path)
agent_profile = env_selector.get_profile(opt_agent_profile)
game = env_selector.get_game(agent_profile.game)
if optimize_for_inference:
agent.disable_training_capability()
self_play_examples = world.generate_self_play(
agent,
game,
games_num,
max_game_steps_n=max_steps,
verbose=verbose,
show_every_turn=debug,
exploration_decay_steps=exploration_decay_steps)
self_play_examples_deque += self_play_examples
serialize(self_play_examples_deque, experience_path)
def __init__(self, **kwargs):
self.steps_done = 0
# -1 represents has not been picked
self.n_agents = kwargs["n_agents"]
self.n_cities = kwargs["n_cities"]
self.steps = kwargs["steps"]
self.conn = kwargs["conn"]
self.tasks = kwargs["tasks"]
self.cities = kwargs["cities"]
self.rewards = kwargs["rewards"]
self.destinations = kwargs["destinations"]
self.budget = kwargs["budget"]
self.world = World(self.n_agents, self.n_cities, self.steps, self.conn,
self.tasks, self.cities, self.rewards,
self.destinations, self.budget)
self.remain_budget = self.get_budget()
self.terminal = self.steps
self.last_pick = [-1] * self.world.n_cities
if not options.random_agent:
parser.error('Random agent profile must be selected')
env_selector = EnvironmentSelector()
agent = env_selector.get_agent(options.agent)
print("Pit with agent ", agent.name)
agent.set_exploration_enabled(False)
random_agent = env_selector.get_agent(options.random_agent)
print("Pit with agent ", random_agent.name)
random_agent.set_exploration_enabled(False)
agent_profile = env_selector.get_profile(options.agent)
game = env_selector.get_game(agent_profile.game)
world = World()
agents = [agent, random_agent]
model_files = [f for f in listdir(options.folder) if isfile(join(options.folder, f)) and f.endswith('h5')]
row_list = []
print_progress_bar(0, len(model_files), prefix='Progress:', suffix='Complete')
for idx, model in enumerate(model_files):
model_path = str(options.folder) + "/" + model
print(model_path)
agent.load(model_path)
result = 0
games_won = 0
agent_second = env_selector.get_agent(options.agent_profile_old)
print("Pit with agent ", agent_second.name)
agent_second.set_exploration_enabled(False)
agent_profile = env_selector.get_profile(options.agent_profile_new)
game = env_selector.get_game(agent_profile.game)
agents = [agent_first, agent_second]
if options.agent_new_path:
agent_first.load(options.agent_new_path)
if options.optimize_for_inference:
agent_first.disable_training_capability(temp_dir="temp",
optimize=True)
if options.agent_old_path:
agent_second.load(options.agent_old_path)
if options.optimize_for_inference:
agent_second.disable_training_capability(temp_dir="temp",
optimize=True)
world = World()
sess_arena_examples, games_results = world.execute_games(
agents,
game,
options.games_num,
max_game_steps_n=options.max_steps,
verbose=options.verbose,
show_every_turn=options.debug)
DELAY = 0
RHO = .005
ANTS = 30
GRASS_COLOR = (252, 233, 88)
ANT_COLOR = (135, 51, 25)
NEST_COLOR = (255, 255, 255)
FOOD_COLOR = (20, 100, 10)
pygame.init()
pygame.mixer.init()
screen = pygame.display.set_mode((SIZE * TAIL_SIZE, SIZE * TAIL_SIZE))
pygame.display.set_caption("Ants")
clock = pygame.time.Clock()
board = World(SIZE)
board.nest((int(SIZE / 2), int(SIZE / 2)))
board.food((10, 10))
for i in range(ANTS):
board.breed()
mapper = Mapper(board, TAIL_SIZE, GRASS_COLOR, ANT_COLOR, NEST_COLOR,
FOOD_COLOR)
running = True
while running:
clock.tick(FPS)
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
import pygame
from Mapper import Mapper
from core.World import World
FPS = 60
SIZE = 201
TAIL = 3
pygame.init()
pygame.mixer.init()
screen = pygame.display.set_mode((SIZE * TAIL, SIZE * TAIL))
pygame.display.set_caption("Ants 2")
clock = pygame.time.Clock()
world = World(SIZE)
# world.pheromones = np.array([
# [0,0,0,0,0,0,0,0,0,0,0], #0
# [0,0,0,0,0,0,0,0,0,0,0], #1
# [0,0,0,0,0,0,0,0,0,0,0], #2
# [0,0,0,0,0,0,0,0,0,0,0], #3
# [0,0,0,0,0,0,0,0,0,0,0], #4
# [0,0,0,0,0,0,0,6,0,6,0], #5
# [0,0,0,0,0,0,0,0,0,0,0], #6
# [0,0,0,0,0,0,0,0,0,0,0], #7
# [0,0,0,0,0,0,0,0,0,0,0], #8
# [0,0,0,0,0,0,0,0,0,0,0], #9
# [0,0,0,0,0,0,0,0,0,0,0], #10
# ])
world.nest = (20, 20, 5)