def main():
log.info('Loading %s...', Game.__name__)
g = Game(4, 9, 4)
log.info('Loading %s...', nn.__name__)
nnet = nn(g)
if args.load_model:
log.info('Loading checkpoint "%s/%s"...', args.load_folder_file[0],
args.load_folder_file[1])
nnet.load_checkpoint(args.load_folder_file[0],
args.load_folder_file[1])
else:
log.warning('Not loading a checkpoint!')
log.info('Loading the Coach...')
c = Coach(g, nnet, args)
if args.load_model:
log.info("Loading 'trainExamples' from file...")
c.loadTrainExamples()
log.info('Starting the learning process 🎉')
c.learn()
nnet=dqn, lr=LR) num_improvements = 0 for _ in range(NUM_EPOCHS): ## Saving a copy of the current network weights to pnet torch.save(coach.nnet.state_dict(), PATH) coach.pnet.load_state_dict(torch.load(PATH)) for _ in tqdm(range(NUM_TRAIN_GAMES), desc="Self play"): data = coach.execute_episode( epsilon=TRAIN_EPSILON, gamma=GAMMA) data = torch.stack(data) coach.learn(data) arena = Arena( player1=lambda x: torch.argmax(coach.nnet(x)), player2=lambda x: torch.argmax(coach.pnet(x)), game=game) ## Introduce small randomness into evaluation games ## Both agents take random moves with small probability p1_won = 0 p2_won = 0 for _ in tqdm(range(HALF), desc="Evaluation 1"): looser = arena.play_game(epsilon=EVAL_EPSILON) if looser == 'red': p2_won += 1 else:
dest='load_model',
action='store_true')
parser.add_argument('-loadf',
'--load_folder_file',
dest='load_folder_file',
type=str)
parser.add_argument('-iterexamp',
'--num_iters_example',
dest='numItersForTrainExamplesHistory',
type=int,
default=20)
args = parser.parse_args()
fh = open(os.path.join("..", "data", "puzzle1.txt"))
fcontent = fh.read()
fh.close()
sys.setrecursionlimit(10000)
g = game(fcontent)
nnet = nn(g, args)
if args.load_model:
nnet.load_checkpoint(args.load_folder_file)
c = Coach(g, nnet, args)
if args.load_model:
print("Load trainExamples from file")
c.loadTrainExamples()
c.learn()
def main():
log.info('Loading Game')
number_of_nodes = 5
# Initialize edges
edges = [(0, 1, {"type": "taxi"}),
(1, 2, {"type": "taxi"}),
(2, 0, {"type": "taxi"}),
(3, 4, {"type": "bus"}),
(3, 4, {"type": "taxi"}),
(3, 4, {"type": "metro"}),
(3, 2, {"type": "ferry"}),
(0, 4, {"type": "taxi"}),
(1, 3, {"type": "taxi"}),
(2, 4, {"type": "taxi"})]
# Assign colors and widths to edges for visualization
for edge in edges:
edge_attributes = edge[2]
if edge_attributes["type"] == 'taxi':
edge_color = (255 / 255, 205 / 255, 66 / 255)
edge_width = 10.0
elif edge_attributes["type"] == 'bus':
edge_color = (23 / 255, 160 / 255, 93 / 255)
edge_width = 6.0
elif edge_attributes["type"] == 'metro':
edge_color = (221 / 255, 80 / 255, 68 / 255)
edge_width = 3.0
elif edge_attributes["type"] == "ferry":
edge_color = 'black'
edge_width = 0.5
else:
edge_color = 'black'
edge_width = 10.0
edge_attributes["color"] = edge_color
edge_attributes["width"] = edge_width
# Initialize players
number_of_detectives = 2
# Set the colors for the different roles (for visualization purposes)
detective_colors = ['green', 'red']
mister_x_color = 'grey'
game = Game(number_of_nodes, edges, number_of_detectives, mister_x_color, detective_colors)
neural_net_mister_x = NNet(game, player=game.mister_x)
game.mister_x.neural_net = neural_net_mister_x
neural_net_detective = NNet(game, game.detectives[0])
for detective in game.detectives:
detective.neural_net = neural_net_detective
"""if args['load_model']:
log.info('Loading checkpoint "%s/%s"...', args['load_folder_file'])
nnet.load_checkpoint(args['load_folder_file'][0], args['load_folder_file'][1])
else:
log.warning('Not loading a checkpoint!')
"""
log.info('Loading the Coach...')
c = Coach(game, neural_net_mister_x, neural_net_detective, args)
"""if args['load_model']:
log.info("Loading 'trainExamples' from file...")
c.loadTrainExamples()
"""
log.info('Starting the learning process 🎉')
c.learn()
network_architecture=configs.network_architecture)
coach = Coach(game=game,
nnet=nnet,
pnet=pnet,
num_iters=configs.num_iters,
root_noise=configs.root_noise,
board_size=configs.board_size)
if configs.load_model:
logging.info("Loading training examples")
coach.loadTrainExamples()
if configs.web_server:
web = WebServer(game=game,
nnet=nnet,
checkpoint_folder=configs.checkpoint_dir,
c_puct=configs.c_puct,
num_mcst_sims=configs.num_mcts_sims)
web.start_web_server()
exit(0)
coach.learn(num_train_episodes=configs.num_episodes,
num_training_examples_to_keep=configs.maxlenOfQueue,
checkpoint_folder=configs.checkpoint_dir,
arena_tournament_size=configs.arena_size,
model_update__win_threshold=configs.update_threshold,
num_mcst_sims=configs.num_mcts_sims,
c_puct=configs.c_puct,
know_nothing_training_iters=configs.tempThreshold,
max_cpus=num_threads)
