def get_experiment_environment(**args):
from utils import setup_mpi_gpus, setup_tensorflow_session
from baselines.common import set_global_seeds
from gym.utils.seeding import hash_seed
process_seed = args["seed"] + 1000 * MPI.COMM_WORLD.Get_rank()
process_seed = hash_seed(process_seed, max_bytes=4)
set_global_seeds(process_seed)
setup_mpi_gpus()
tf_context = setup_tensorflow_session()
return tf_context
def get_experiment_environment(**args):
from utils import setup_mpi_gpus, setup_tensorflow_session
from baselines.common import set_global_seeds
from gym.utils.seeding import hash_seed
process_seed = args["seed"] + 1000 * MPI.COMM_WORLD.Get_rank()
process_seed = hash_seed(process_seed, max_bytes=4)
set_global_seeds(process_seed)
setup_mpi_gpus()
logger_context = logger.scoped_configure(dir=None,
format_strs=['stdout', 'log',
'csv'] if MPI.COMM_WORLD.Get_rank() == 0 else ['log'])
tf_context = setup_tensorflow_session()
return logger_context, tf_context
def start_experiment(**args):
from utils import setup_mpi_gpus
setup_mpi_gpus()
make_env = partial(make_env_all_params, add_monitor=True, args=args)
trainer = Trainer(make_env=make_env,
num_timesteps=args['num_timesteps'],
hps=args,
envs_per_process=args['envs_per_process'])
log, tf_sess = get_experiment_environment(**args)
with log, tf_sess:
logdir = logger.get_dir()
print("results will be saved to ", logdir)
trainer.train()
def get_experiment_environment(**args):
process_seed = 1234 + 1000 * MPI.COMM_WORLD.Get_rank()
process_seed = hash_seed(process_seed, max_bytes=4)
set_global_seeds(1234)
setup_mpi_gpus()
logger_context = logger.scoped_configure(
dir='C:/Users/Elias/Desktop/savedunc/' + MODE + '_' +
datetime.now().strftime('%Y_%m_%d_%H_%M_%S'),
format_strs=['stdout', 'log', 'csv', 'tensorboard']
if MPI.COMM_WORLD.Get_rank() == 0 else ['log'])
tf_context = setup_tensorflow_session()
return logger_context, tf_context
def get_experiment_environment(**args):
from utils import setup_mpi_gpus, setup_tensorflow_session
from baselines.common import set_global_seeds
from gym.utils.seeding import hash_seed
process_seed = args["seed"] + 1000 * MPI.COMM_WORLD.Get_rank()
process_seed = hash_seed(process_seed, max_bytes=4)
set_global_seeds(process_seed)
setup_mpi_gpus()
logdir = './' + args["logdir"] + '/' + datetime.datetime.now().strftime(
args["expID"] + "-openai-%Y-%m-%d-%H-%M-%S-%f")
logger_context = logger.scoped_configure(
dir=logdir,
format_strs=['stdout', 'log', 'csv', 'tensorboard']
if MPI.COMM_WORLD.Get_rank() == 0 else ['log'])
tf_context = setup_tensorflow_session()
return logger_context, tf_context, logdir
def start_score(**args):
from utils import setup_mpi_gpus
setup_mpi_gpus()
make_env = partial(make_env_all_params,
add_monitor=True,
args=args,
sleep_multiple=0)
scorer = Scorer(make_env=make_env,
num_timesteps=args['num_timesteps'],
hps=args,
envs_per_process=args['envs_per_process'])
log, tf_sess = get_experiment_environment(**args)
with log, tf_sess:
logdir = logger.get_dir()
scorer.score()
def get_experiment_environment(**args):
from utils import setup_mpi_gpus, setup_tensorflow_session
from baselines.common import set_global_seeds
from gym.utils.seeding import hash_seed
process_seed = args["seed"] + 1000 * MPI.COMM_WORLD.Get_rank()
process_seed = hash_seed(process_seed, max_bytes=4)
set_global_seeds(process_seed)
setup_mpi_gpus()
time = datetime.datetime.now().strftime("%m-%d-%H-%M-%S")
path_with_args = './logs/' + '_'.join([
time, args['exp_name'], args['env_kind'], args['feature_space'],
str(args['envs_per_process']),
str(args['train_discriminator']),
str(args['discriminator_weighted'])
])
format_strs = ['stdout', 'log', 'csv', 'tensorboard'
] if MPI.COMM_WORLD.Get_rank() == 0 else ['log']
logger_context = logger.scoped_configure(dir=path_with_args,
format_strs=format_strs)
tf_context = setup_tensorflow_session()
return logger_context, tf_context
def get_experiment_environment(**args):
# 初始化 MPI 相关的量
from utils import setup_mpi_gpus, setup_tensorflow_session
from baselines.common import set_global_seeds
from gym.utils.seeding import hash_seed
process_seed = args["seed"] + 1000 * MPI.COMM_WORLD.Get_rank()
process_seed = hash_seed(process_seed, max_bytes=4)
set_global_seeds(process_seed)
setup_mpi_gpus()
logger_dir = './logs/' + datetime.datetime.now().strftime(
args["env"] + "-" + args["reward_type"] + "-" +
str(args["nepochs_dvae"]) + "-" + str(args["stickyAtari"]) +
"-%Y-%m-%d-%H-%M-%S-%f")
logger_context = logger.scoped_configure(
dir=logger_dir,
format_strs=['stdout', 'log', 'csv']
if MPI.COMM_WORLD.Get_rank() == 0 else ['log'])
tf_context = setup_tensorflow_session()
# bai. 新增 saver 用于保存权重
saver = tf.train.Saver()
return logger_context, tf_context, saver, logger_dir
#env.render() from utils import * from collections import deque import gym import cv2 import os import coinrun.main_utils as utils from coinrun import setup_utils, policies, wrappers, ppo2 from coinrun.config import Config #from gym.envs.classic_control import rendering from collections import deque import random from image_bco import ImageBCO utils.setup_mpi_gpus() setup_utils.setup_and_load() game = utils.make_general_env(1) game = wrappers.add_final_wrappers(game) game.reset() args.checkpoint = 'coin_ilpo' args.input_dir = 'final_models/coin' args.exp_dir = 'results/final_coin_bco' args.n_actions = 4 args.real_actions = 4 args.policy_lr = .0001 args.batch_size = 100 args.ngf = 15 states = [] next_states = []
